Distances on a one-dimensional lattice from noncommutative geometry

In the following paper we continue the work of Bimonte-Lizzi-Sparano on distances on a one dimensional lattice. We succeed in proving analytically the exact formulae for such distances. We find that the distance to an even point on the lattice is the geometrical average of the ``predecessor'' and ``successor'' distances to the neighbouring odd points.Comment: LaTeX file, few minor typos corrected, 9 page

Nernst Effect as a Signature of Quantum Fluctuations in Quasi-1D Superconductors

We study a model for the transverse thermoelectric response due to quantum superconducting fluctuations in a two-leg Josephson ladder, subject to a perpendicular magnetic field B and a transverse temperature gradient. The off-diagonal Peltier coefficient (\alpha_{xy}) and the Nernst effect are evaluated as functions of B and the temperature T. The Nernst effect is found to exhibit a prominent peak close to the superconductor-insulator transition (SIT), which becomes progressively enhanced at low T. In addition, we derive a relation to diamagnetic response: \alpha_{xy}= -M/T_0, where M is the equilibrium magnetization and T_0 a plasma energy in the superconducting legs.Comment: An extended (and hopefully more comprehensible) version of an earlier postin

Pythagoras' Theorem on a 2D-Lattice from a "Natural" Dirac Operator and Connes' Distance Formula

One of the key ingredients of A. Connes' noncommutative geometry is a generalized Dirac operator which induces a metric(Connes' distance) on the state space. We generalize such a Dirac operator devised by A. Dimakis et al, whose Connes' distance recovers the linear distance on a 1D lattice, into 2D lattice. This Dirac operator being "naturally" defined has the so-called "local eigenvalue property" and induces Euclidean distance on this 2D lattice. This kind of Dirac operator can be generalized into any higher dimensional lattices.Comment: Latex 11pages, no figure

Dirac Operators and the Calculation of the Connes Metric on arbitrary (Infinite) Graphs

As an outgrowth of our investigation of non-regular spaces within the context of quantum gravity and non-commutative geometry, we develop a graph Hilbert space framework on arbitrary (infinite) graphs and use it to study spectral properties of graph-Laplacians and graph-Dirac-operators. We define a spectral triplet sharing most of the properties of what Connes calls a spectral triple. With the help of this scheme we derive an explicit expression for the Connes-distance function on general directed or undirected graphs. We derive a series of apriori estimates and calculate it for a variety of examples of graphs. As a possibly interesting aside, we show that the natural setting of approaching such problems may be the framework of (non-)linear programming or optimization. We compare our results (arrived at within our particular framework) with the results of other authors and show that the seeming differences depend on the use of different graph-geometries and/or Dirac operators.Comment: 27 pages, Latex, comlementary to an earlier paper, general treatment of directed and undirected graphs, in section 4 a series of general results and estimates concerning the Connes Distance on graphs together with examples and numerical estimate

A genetic contribution from the Far East into Ashkenazi Jews via the ancient Silk Road

Contemporary Jews retain a genetic imprint from their Near Eastern ancestry, but obtained substantial genetic components from their neighboring populations during their history. Whether they received any genetic contribution from the Far East remains unknown, but frequent communication with the Chinese has been observed since the Silk Road period. To address this issue, mitochondrial DNA (mtDNA) variation from 55,595 Eurasians are analyzed. The existence of some eastern Eurasian haplotypes in eastern Ashkenazi Jews supports an East Asian genetic contribution, likely from Chinese. Further evidence indicates that this connection can be attributed to a gene flow event that occurred less than 1.4 kilo-years ago (kya), which falls within the time frame of the Silk Road scenario and fits well with historical records and archaeological discoveries. This observed genetic contribution from Chinese to Ashkenazi Jews demonstrates that the historical exchange between Ashkenazim and the Far East was not confined to the cultural sphere but also extended to an exchange of genes

Comprehensive microRNA profiling in B-cells of human centenarians by massively parallel sequencing

Background: MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression and play a critical role in development, homeostasis, and disease. Despite their demonstrated roles in age-associated pathologies, little is known about the role of miRNAs in human aging and longevity.Results: We employed massively parallel sequencing technology to identify miRNAs expressed in B-cells from Ashkenazi Jewish centenarians, i.e., those living to a hundred and a human model of exceptional longevity, and younger controls without a family history of longevity. With data from 26.7 million read

Phase separation during film growth

A diffusion equation describing phase separation during co‐deposition of a binary alloy is derived, and solved in the limit of dominant surface diffusion. Linear stability analysis yields results similar to bulk spinodal decomposition, except that long, and possibly all, wavelength are stabilized. Decomposition into two phases is investigated by solving the diffusion equation for lamellar and cylindrical symmetry. For the lamellar geometry, typically observed for near‐equal volume fractions, the diffusion equation does not yield wavelength selection criteria. These can be obtained if free energy minimization is assumed. For the cylindrical geometry, solutions for small volume fractions yield domain dimensions proportional to the deposition‐rate dependent surface diffusion length.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71165/2/JAPIAU-72-2-442-1.pd

Epitaxial growth in dislocation-free strained alloy films: Morphological and compositional instabilities

The mechanisms of stability or instability in the strained alloy film growth are of intense current interest to both theorists and experimentalists. We consider dislocation-free, coherent, growing alloy films which could exhibit a morphological instability without nucleation. We investigate such strained films by developing a nonequilibrium, continuum model and by performing a linear stability analysis. The couplings of film-substrate misfit strain, compositional stress, deposition rate, and growth temperature determine the stability of film morphology as well as the surface spinodal decomposition. We consider some realistic factors of epitaxial growth, in particular the composition dependence of elastic moduli and the coupling between top surface and underlying bulk of the film. The interplay of these factors leads to new stability results. In addition to the stability diagrams both above and below the coherent spinodal temperature, we also calculate the kinetic critical thickness for the onset of instability as well as its scaling behavior with respect to misfit strain and deposition rate. We apply our results to some real growth systems and discuss the implications related to some recent experimental observations.Comment: 26 pages, 13 eps figure

"This is my unicorn, Fluffy": Personalizing frozen vision-language representations

Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for reasoning about user-specific visual concepts in unstructured language. This problem arises in multiple domains, from personalized image retrieval to personalized interaction with smart devices. We introduce a new learning setup called Personalized Vision & Language (PerVL) with two new benchmark datasets for retrieving and segmenting user-specific "personalized" concepts "in the wild". In PerVL, one should learn personalized concepts (1) independently of the downstream task (2) allowing a pretrained model to reason about them with free language, and (3) does not require personalized negative examples. We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts. The model can then reason about them by simply using them in a sentence. We demonstrate that our approach learns personalized visual concepts from a few examples and can effectively apply them in image retrieval and semantic segmentation using rich textual queries