Raman response of Stage-1 graphite intercalation compounds revisited

We present a detailed in-situ Raman analysis of stage-1 KC8, CaC6, and LiC6 graphite intercalation compounds (GIC) to unravel their intrinsic finger print. Four main components were found between 1200 cm-1 and 1700 cm-1, and each of them were assigned to a corresponding vibrational mode. From a detailed line shape analysis of the intrinsic Fano-lines of the G- and D-line response we precisely determine the position ({\omega}ph), line width ({\Gamma}ph) and asymmetry (q) from each component. The comparison to the theoretical calculated line width and position of each component allow us to extract the electron-phonon coupling constant of these compounds. A coupling constant {\lambda}ph < 0.06 was obtained. This highlights that Raman active modes alone are not sufficient to explain the superconductivity within the electron-phonon coupling mechanism in CaC6 and KC8.Comment: 6 pages, 3 figures, 2 table

Two-electronic component behavior in the multiband FeSe0.42_{0.42}Te0.58_{0.58} superconductor

We report X-band EPR and $^{125}$Te and $^{77}$Se NMR measurements on single-crystalline superconducting FeSe$_{0.42}$Te$_{0.58}$ ($T_c$ = 11.5(1) K). The data provide evidence for the coexistence of intrinsic localized and itinerant electronic states. In the normal state, localized moments couple to itinerant electrons in the Fe(Se,Te) layers and affect the local spin susceptibility and spin fluctuations. Below $T_c$, spin fluctuations become rapidly suppressed and an unconventional superconducting state emerges in which $1/T_1$ is reduced at a much faster rate than expected for conventional $s$- or $s_\pm$-wave symmetry. We suggest that the localized states arise from the strong electronic correlations within one of the Fe-derived bands. The multiband electronic structure together with the electronic correlations thus determine the normal and superconducting states of the FeSe$_{1-x}$Te$_x$ family, which appears much closer to other high-$T_c$ superconductors than previously anticipated.Comment: 5 pages, 4 figure

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Jahn-Teller orbital glass state in the expanded fcc Cs3C60 fulleride

The most expanded fcc-structured alkali fulleride, Cs3C60, is a Mott insulator at ambient pressure because of the weak overlap between the frontier t1u molecular orbitals of the C603− anions. It has a severely disordered antiferromagnetic ground state that becomes a superconductor with a high critical temperature, Tc of 35 K upon compression. The effect of the localised t1u3 electronic configuration on the properties of the material is not well-understood. Here we study the relationship between the intrinsic crystallographic C603− orientational disorder and the molecular Jahn–Teller (JT) effect dynamics in the Mott insulating state. The high-resolution 13C magic-angle-spinning (MAS) NMR spectrum at room temperature comprises three peaks in the intensity ratio 1:2:2 consistent with the presence of three crystallographically-inequivalent carbon sites in the fcc unit cell and revealing that the JT-effect dynamics are fast on the NMR time-scale of 10−5 s despite the presence of the frozen-in C603− merohedral disorder disclosed by the 133Cs MAS NMR fine splitting of the tetrahedral and octahedral 133Cs resonances. Cooling to sub-liquid-nitrogen temperatures leads to severe broadening of both the 13C and 133Cs MAS NMR multiplets, which provides the signature of an increased number of inequivalent 13C and 133Cs sites. This is attributed to the freezing out of the C603− JT dynamics and the development of a t1u electronic orbital glass state guided by the merohedral disorder of the fcc structure. The observation of the dynamic and static JT effect in the Mott insulating state of the metrically cubic but merohedrally disordered Cs3C60 fulleride in different temperature ranges reveals the intimate relation between charge localization, magnetic ground state, lifting of electronic degeneracy, and orientational disorder in these strongly-correlated systems

Strong electron correlations in the normal state of FeSe0.42Te0.58

We investigate the normal state of the '11' iron-based superconductor FeSe0.42Te0.58 by angle resolved photoemission. Our data reveal a highly renormalized quasiparticle dispersion characteristic of a strongly correlated metal. We find sheet dependent effective carrier masses between ~ 3 - 16 m_e corresponding to a mass enhancement over band structure values of m*/m_band ~ 6 - 20. This is nearly an order of magnitude higher than the renormalization reported previously for iron-arsenide superconductors of the '1111' and '122' families but fully consistent with the bulk specific heat.Comment: 5 pages, 4 figures, to appear in Phys. Rev. Let

On the Effectiveness of Image Rotation for Open Set Domain Adaptation

Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first separating the known/unknown target samples and then aligning known target samples with the source data. We propose a novel method to addresses both these problems using the self-supervised task of rotation recognition. Moreover, we assess the performance with a new open set metric that properly balances the contribution of recognizing the known classes and rejecting the unknown samples. Comparative experiments with existing OSDA methods on the standard Office-31 and Office-Home benchmarks show that: (i) our method outperforms its competitors, (ii) reproducibility for this field is a crucial issue to tackle, (iii) our metric provides a reliable tool to allow fair open set evaluation.Comment: accepted at ECCV 202

U-DADA:Unsupervised Deep Action Domain Adaptation

The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.</p

Evidence for phase formation in potassium intercalated 1,2;8,9-dibenzopentacene

We have prepared potassium intercalated 1,2;8,9-dibenzopentacene films under vacuum conditions. The evolution of the electronic excitation spectra upon potassium addition as measured using electron energy-loss spectroscopy clearly indicate the formation of particular doped phases with compositions K$_x$dibenzopentacene ($x$ = 1,2,3). Moreover, the stability of these phases as a function of temperature has been explored. Finally, the electronic excitation spectra also give insight into the electronic ground state of the potassium doped 1,2;8,9-dibenzopentacene films.Comment: 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:1201.200

Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subjectComment: Accepted at MICCAI 202