Giant Collective Spin-Orbit Field in a Quantum Well: Fine Structure of Spin Plasmons

We employ inelastic light scattering with magnetic fields to study intersubband spin plasmons in a quantum well. We demonstrate the existence of a giant collective spin-orbit (SO) field that splits the spin-plasmon spectrum into a triplet. The effect is remarkable as each individual electron would be expected to precess in its own momentum-dependent SO field, leading to D'yakonov-Perel' dephasing. Instead, many-body effects lead to a striking organization of the SO fields at the collective level. The macroscopic spin moment is quantized by a uniform collective SO field, five times higher than the individual SO field. We provide a momentum-space cartography of this field.Comment: 5 pages, 4 figures. Supplemental material available here as an ancillary fil

Coulomb-driven organization and enhancement of spin-orbit fields in collective spin excitations

Spin-orbit (SO) fields in a spin-polarized electron gas are studied by angle-resolved inelastic light scattering on a CdMnTe quantum well. We demonstrate a striking organization and enhancement of SO fields acting on the collective spin excitation (spin-flip wave). While individual electronic SO fields have a broadly distributed momentum dependence, giving rise to D'yakonov-Perel' dephasing, the collective spin dynamics is governed by a single collective SO field which is drastically enhanced due to many-body effects. The enhancement factor is experimentally determined. These results provide a powerful indication that these constructive phenomena are universal to collective spin excitations of conducting systems.Comment: 5 pages, 4 figure

Dynamical Corrections to Spin Wave Excitations in Quantum Wells due to Coulomb Interactions and Magnetic Ions

We have measured dispersions of spin-flip waves and spin-flip single-particle excitations of a spin polarized two-dimensional electron gas in a CdMnTe quantum well using resonant Raman scattering. We find the energy of the spin-flip wave to be below the spin-flip single particle excitation continuum, a contradiction to the theory of spin waves in diluted magnetic semiconductors put forth in [Phys. Rev. B 70, 045205 (2004)]. We show that the inclusion of terms accounting for the Coulomb interaction between carriers in the spin wave propagator leads to an agreement with our experimental results. The dominant Coulomb contribution leads to an overall red shift of the mixed electron-Mn spin modes while the dynamical coupling between Mn ions results in a small blue shift. We provide a simulated model system which shows the reverse situation but at an extremely large magnetic field.Comment: 6 pages, 7 figure

On Graphs Coverable by k Shortest Paths

We show that if the edges or vertices of an undirected graph G can be covered by k shortest paths, then the pathwidth of G is upper-bounded by a function of k. As a corollary, we prove that the problem Isometric Path Cover with Terminals (which, given a graph G and a set of k pairs of vertices called terminals, asks whether G can be covered by k shortest paths, each joining a pair of terminals) is FPT with respect to the number of terminals. The same holds for the similar problem Strong Geodetic Set with Terminals (which, given a graph G and a set of k terminals, asks whether there exist binom(k,2) shortest paths, each joining a distinct pair of terminals such that these paths cover G). Moreover, this implies that the related problems Isometric Path Cover and Strong Geodetic Set (defined similarly but where the set of terminals is not part of the input) are in XP with respect to parameter k

Chirality and intrinsic dissipation of spin modes in two-dimensional electron liquids

We review recent theoretical and experimental developments concerning collective spin excitations in two-dimensional electron liquid (2DEL) systems, with particular emphasis on the interplay between many-body and spin-orbit effects, as well as the intrinsic dissipation due to the spin-Coulomb drag. Historically, the experimental realization of 2DELs in silicon inversion layers in the 60s and 70s created unprecedented opportunities to probe subtle quantum effects, culminating in the discovery of the quantum Hall effect. In the following years, high quality 2DELs were obtained in doped quantum wells made in typical semiconductors like GaAs or CdTe. These systems became important test beds for quantum many-body effects due to Coulomb interaction, spin dynamics, spin-orbit coupling, effects of applied magnetic fields, as well as dissipation mechanisms. Here we focus on the recent results involving chiral effects and intrinsic dissipation of collective spin modes: these are not only of fundamental interest but also important towards demonstrating new concepts in spintronics. Moreover, new realizations of 2DELs are emerging beyond traditional semiconductors, for instance in multilayer graphene, oxide interfaces, dichalcogenide monolayers, and many more. The concepts discussed in this review will be relevant also for these emerging systems.Comment: Topical review. 28 pages, 18 figure

PLSA-based Image Auto-Annotation: Constraining the Latent Space

We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of modeling multi-modal co-occurrences, constraining the definition of the latent space to ensure its consistency in semantic terms (words), while retaining the ability to jointly model visual information. The concept is implemented by a linked pair of Probabilistic Latent Semantic Analysis (PLSA) models. On a 16000-image collection, we show with extensive experiments and using various performance measures, that our approach significantly outperforms previous joint models

On Image Auto-Annotation with Latent Space Models

Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of text and image features have shown encouraging results, but their performance with respect to other approaches remains unknown. In this paper, we apply and compare two simple latent space models commonly used in text analysis, namely Latent Semantic Analysis (LSA) and Probabilistic LSA (PLSA). Annotation strategies for each model are discussed. Remarkably, we found that, on a 8000-image dataset, a classic LSA model defined on keywords and a very basic image representation performed as well as much more complex, state-of-the-art methods. Furthermore, non-probabilistic methods (LSA and direct image matching) outperformed PLSA on the same dataset

On Automatic Annotation of Images with Latent Space Models

Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of text and image features have shown encouraging results, but their performance with respect to other approaches remains unknown. In this paper, we apply and compare two simple latent space models commonly used in text analysis, namely Latent Semantic Analysis (LSA) and Probabilistic LSA (PLSA). Annotation strategies for each model are discussed. Remarkably, we found that, on a 8000-image dataset, a classic LSA model defined on keywords and a very basic image representation performed as well as much more complex, state-of-the-art methods. Furthermore, non-probabilistic methods (LSA and direct image matching) outperformed PLSA on the same dataset

Modeling semantic aspects for cross-media image indexing

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework