Probing the phase diagram of CeRu_2Ge_2 by thermopower at high pressure

The temperature dependence of the thermoelectric power, S(T), and the electrical resistivity of the magnetically ordered CeRu_2Ge_2 (T_N=8.55 K and T_C=7.40 K) were measured for pressures p < 16 GPa in the temperature range 1.2 K < T < 300 K. Long-range magnetic order is suppressed at a p_c of approximately 6.4 GPa. Pressure drives S(T) through a sequence of temperature dependences, ranging from a behaviour characteristic for magnetically ordered heavy fermion compounds to a typical behaviour of intermediate-valent systems. At intermediate pressures a large positive maximum develops above 10 K in S(T). Its origin is attributed to the Kondo effect and its position is assumed to reflect the Kondo temperature T_K. The pressure dependence of T_K is discussed in a revised and extended (T,p) phase diagram of CeRu_2Ge_2.Comment: 7 pages, 6 figure

From spin-Peierls to superconductivity: (TMTTF)_2PF_6 under high pressure

The nature of the attractive electron-electron interaction, leading to the formation of Cooper-pairs in unconventional superconductors has still to be fully understood and is subject to intensive research. Here we show that the sequence spin-Peierls, antiferromagnetism, superconductivity observed in (TMTTF)_2PF_6 under pressure makes the (TM)_2X phase diagram universal. We argue that the suppression of the spin-Peierls transition under pressure, the close vicinity of antiferromagnetic and superconducting phases at high pressure as well as the existence of critical antiferromagnetic fluctuations above T_c strongly support the intriguing possibility that the interchain exchange of antiferromagnetic fluctuations provides the pairing mechanism required for bound charge carriers.Comment: 4 pages, revtex, 4 figures (jpeg,eps,png

Probing the extended non-Fermi liquid regimes of MnSi and Fe

Recent studies show that the non-Fermi liquid (NFL) behavior of MnSi and Fe spans over an unexpectedly broad pressure range, between the critical pressure p_c and around 2p_c. In order to determine the extension of their NFL regions, we analyze the evolution of the resistivity rho(T) A(p)T^n at higher pressures. We find that in MnSi the n=3/2 exponent holds below 4.8 GPa=3 p_c, but it increases above that pressure. At 7.2 GPa we observe the low temperature Fermi liquid exponent n=2 whereas for T>1.5 K, n=5/3. Our measurements in Fe show that the NFL behavior rho T^{5/3} extends at least up to 30.5 GPa, above the entire superconducting (SC) region. In the studied pressure range, the onset of the SC transition reduces by a factor 10 down to T_c^onset(30.5 GPa)=0.23 K, while the A-coefficient diminishes monotonically by around 50%.Comment: 2 pages, 2 figures, Proceedings SCES 200

Epitaxial growth and transport properties of Nb-doped SrTiO3_{3} thin films

Nb-doped SrTiO$_{3}$ epitaxial thin films have been prepared on (001) SrTiO$_{3}$ substrates using pulsed laser deposition. A high substrate temperature ($>1000^{\circ}{C}$) was found to be necessary to achieve 2-dimensional growth. Atomic force microscopy reveals atomically flat surfaces with 3.9 \AA $$ steps. The films show a metallic behavior, residual resistivity ratios between 10 and 100, and low residual resistivity of the order of 10$^{-4}$$\Omega$cm. At 0.3 K, a sharp superconducting transition, reaching zero resistance, is observed.Comment: 4 pages, 4 figure

Theory of the thermoelectricity of intermetallic compounds with Ce or Yb ions

The thermoelectric properties of intermetallic compounds with Ce or Yb ions are explained by the single-impurity Anderson model which takes into account the crystal-field splitting of the 4{\it f} ground-state multiplet, and assumes a strong Coulomb repulsion which restricts the number of {\it f} electrons or {\it f} holes to $n_f\leq 1$ for Ce and $n_f^{hole}\leq 1$ for Yb ions. Using the non-crossing approximation and imposing the charge neutrality constraint on the local scattering problem at each temperature and pressure, the excitation spectrum and the transport coefficients of the model are obtained. The thermopower calculated in such a way exhibits all the characteristic features observed in Ce and Yb intermetallics. Calculating the effect of pressure on various characteristic energy scales of the model, we obtain the $(T,p)$ phase diagram which agrees with the experimental data on CeRu$_{2}$Si$_2$, CeCu$_{2}$Si$_2$, CePd$_{2}$Si$_2$, and similar compounds. The evolution of the thermopower and the electrical resistance as a function of temperature, pressure or doping is explained in terms of the crossovers between various fixed points of the model and the redistribution of the single-particle spectral weight within the Fermi window.Comment: 13 pages, 11 figure

High-pressure transport properties of CeRu_2Ge_2

The pressure-induced changes in the temperature-dependent thermopower S(T) and electrical resistivity \rho(T) of CeRu_2Ge_2 are described within the single-site Anderson model. The Ce-ions are treated as impurities and the coherent scattering on different Ce-sites is neglected. Changing the hybridisation \Gamma between the 4f-states and the conduction band accounts for the pressure effect. The transport coefficients are calculated in the non-crossing approximation above the phase boundary line. The theoretical S(T) and \rho(T) curves show many features of the experimental data. The seemingly complicated temperature dependence of S(T) and \rho(T), and their evolution as a function of pressure, is related to the crossovers between various fixed points of the model.Comment: 9 pages, 10 figure

Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier

Existing generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.Publishe

Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/