Structural and Superconducting Properties of RbOs2O6 Single Crystals

Single crystals of RbOs2O6 have been grown from Rb2O and Os in sealed quartz ampoules. The crystal structure has been identified at room temperature as cubic with the lattice constant a = 10.1242(12) A. The anisotropy of the tetrahedral and octahedral networks is lower and the displacement parameters of alkali metal atoms are smaller than for KOs2O6, so the "rattling" of the alkali atoms in RbOs2O6 is less pronounced. Superconducting properties of RbOs2O6 in the mixed state have been well described within the London approach and the Ginzburg-Landau parameter kappa(0) = 31 has been derived from the reversible magnetization. This parameter is field dependent and changes at low temperatures from kappa = 22 (low fields) to kappa = 31 at H_{c2}. The thermodynamic critical field H_{c}(0) = 1.3 kOe and the superconducting gap 2delta/k_{B}T_{c} = 3.2 have been estimated. These results together with slightly different H_{c2}(T) dependence obtained for crystals and polycrystalline RbOs2O6 proof evidently that this compound is a weak-coupling BCS-type superconductor close to the dirty limit.Comment: 20 pages, 8 figures, 3 table

Fast mode decomposition in few-mode fibers

Retrieval of the optical phase information from measurement of intensity is of a high interest because this would facilitate simple and cost-efficient techniques and devices. In scientific and industrial applications that exploit multi-mode fibers, a prior knowledge of spatial mode structure of the fiber, in principle, makes it possible to recover phases using measured intensity distribution. However, current mode decomposition algorithms based on the analysis of the intensity distribution at the output of a few-mode fiber, such as optimization methods or neural networks, still have high computational costs and high latency that is a serious impediment for applications, such as telecommunications. Speed of signal processing is one of the key challenges in this approach. We present a high-performance mode decomposition algorithm with a processing time of tens of microseconds. The proposed mathematical algorithm that does not use any machine learning techniques, is several orders of magnitude faster than the state-of-the-art deep-learning-based methods. We anticipate that our results can stimulate further research on algorithms beyond popular machine learning methods and they can lead to the development of low-cost phase retrieval receivers for various applications of few-mode fibers ranging from imaging to telecommunications

A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes

Humans can categorize objects in complex natural scenes within 100–150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ∼6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization

Human Visual Search Does Not Maximize the Post-Saccadic Probability of Identifying Targets

Researchers have conjectured that eye movements during visual search are selected to minimize the number of saccades. The optimal Bayesian eye movement strategy minimizing saccades does not simply direct the eye to whichever location is judged most likely to contain the target but makes use of the entire retina as an information gathering device during each fixation. Here we show that human observers do not minimize the expected number of saccades in planning saccades in a simple visual search task composed of three tokens. In this task, the optimal eye movement strategy varied, depending on the spacing between tokens (in the first experiment) or the size of tokens (in the second experiment), and changed abruptly once the separation or size surpassed a critical value. None of our observers changed strategy as a function of separation or size. Human performance fell far short of ideal, both qualitatively and quantitatively

Parallel Mechanisms for Visual Search in Zebrafish

This research was funded by project grant G1000053 from the National Centre for the Replacement, Reduction and Refinement of animals in research (NC3Rs; UK) and by the Medical Research Council (MRC; UK). CHB is a Royal Society (UK) Research Fellow

Overt Visual Attention as a Causal Factor of Perceptual Awareness

Our everyday conscious experience of the visual world is fundamentally shaped by the interaction of overt visual attention and object awareness. Although the principal impact of both components is undisputed, it is still unclear how they interact. Here we recorded eye-movements preceding and following conscious object recognition, collected during the free inspection of ambiguous and corresponding unambiguous stimuli. Using this paradigm, we demonstrate that fixations recorded prior to object awareness predict the later recognized object identity, and that subjects accumulate more evidence that is consistent with their later percept than for the alternative. The timing of reached awareness was verified by a reaction-time based correction method and also based on changes in pupil dilation. Control experiments, in which we manipulated the initial locus of visual attention, confirm a causal influence of overt attention on the subsequent result of object perception. The current study thus demonstrates that distinct patterns of overt attentional selection precede object awareness and thereby directly builds on recent electrophysiological findings suggesting two distinct neuronal mechanisms underlying the two phenomena. Our results emphasize the crucial importance of overt visual attention in the formation of our conscious experience of the visual world

SHARPIN Is Essential for Cytokine Production, NF-κB Signaling, and Induction of Th1 Differentiation by Dendritic Cells

Spontaneous mutations of the Sharpin (SHANK-associated RH domain-interacting protein, other aliases: Rbckl1, Sipl1) gene in mice result in systemic inflammation that is characterized by chronic proliferative dermatitis and dysregulated secretion of T helper1 (Th1) and Th2 cytokines. The cellular and molecular mechanisms underlying this inflammatory phenotype remain elusive. Dendritic cells may contribute to the initiation and progression of the phenotype of SHARPIN-deficient mice because of their pivotal role in innate and adaptive immunity. Here we show by flow cytometry that SHARPIN- deficiency did not alter the distribution of different DC subtypes in the spleen. In response to TOLL-like receptor (TLR) agonists LPS and poly I:C, cultured bone marrow-derived dendritic cells (BMDC) from WT and mutant mice exhibited similar increases in expression of co-stimulatory molecules CD40, CD80, and CD86. However, stimulated SHARPIN-deficient BMDC had reduced transcription and secretion of pro-inflammatory mediators IL6, IL12P70, GMCSF, and nitric oxide. Mutant BMDC had defective activation of NF-κB signaling, whereas the MAPK1/3 (ERK1/2) and MAPK11/12/13/14 (p38 MAP kinase isoforms) and TBK1 signaling pathways were intact. A mixed lymphocyte reaction showed that mutant BMDC only induced a weak Th1 immune response but stimulated increased Th2 cytokine production from allogeneic naïve CD4+ T cells. In conclusion, loss of Sharpin in mice significantly affects the immune function of DC and this may partially account for the systemic inflammation and Th2-biased immune response

The contributions of image content and behavioral relevancy to overt attention

During free-viewing of natural scenes, eye movements are guided by bottom-up factors inherent to the stimulus, as well as top-down factors inherent to the observer. The question of how these two different sources of information interact and contribute to fixation behavior has recently received a lot of attention. Here, a battery of 15 visual stimulus features was used to quantify the contribution of stimulus properties during free-viewing of 4 different categories of images (Natural, Urban, Fractal and Pink Noise). Behaviorally relevant information was estimated in the form of topographical interestingness maps by asking an independent set of subjects to click at image regions that they subjectively found most interesting. Using a Bayesian scheme, we computed saliency functions that described the probability of a given feature to be fixated. In the case of stimulus features, the precise shape of the saliency functions was strongly dependent upon image category and overall the saliency associated with these features was generally weak. When testing multiple features jointly, a linear additive integration model of individual saliencies performed satisfactorily. We found that the saliency associated with interesting locations was much higher than any low-level image feature and any pair-wise combination thereof. Furthermore, the low-level image features were found to be maximally salient at those locations that had already high interestingness ratings. Temporal analysis showed that regions with high interestingness ratings were fixated as early as the third fixation following stimulus onset. Paralleling these findings, fixation durations were found to be dependent mainly on interestingness ratings and to a lesser extent on the low-level image features. Our results suggest that both low- and high-level sources of information play a significant role during exploration of complex scenes with behaviorally relevant information being more effective compared to stimulus features.publisher versio