A Dynamic Programming Solution to Bounded Dejittering Problems

We propose a dynamic programming solution to image dejittering problems with bounded displacements and obtain efficient algorithms for the removal of line jitter, line pixel jitter, and pixel jitter.Comment: The final publication is available at link.springer.co

Reliability Evaluation for Clustered WSNs under Malware Propagation.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN

A parametric description of the 3D structure of the Galactic bar/bulge using the VVV survey

© 2017 The Authors. We study the structure of the inner Milky Way using the latest data release of the VISTA Variables in the Via Lactea (VVV) survey. The VVV is a deep near-infrared, multi-colour photometric survey with a coverage of 300 square degrees towards the bulge/bar. We use red clump (RC) stars to produce a high-resolution dust map of the VVV's field of view. From dereddened colour-magnitude diagrams, we select red giant branch stars to investigate their 3D density distribution within the central 4 kpc. We demonstrate that our best-fitting parametric model of the bulge density provides a good description of the VVV data, with a median percentage residual of 5 per cent over the fitted region. The strongest of the otherwise lowlevel residuals are overdensities associated with a low-latitude structure as well as the so-called X-shape previously identified using the split RC. These additional components contribute only ~5 per cent and ~7 per cent respectively to the bulge mass budget. The best-fitting bulge is 'boxy' with an axial ratio of [1:0.44:0.31] and is rotated with respect to the Sun-Galactic Centre line by at least 20°. We provide an estimate of the total, full sky, mass of the bulge of MbulgeChabrier= 2.36 × 1010M⊙for a Chabrier initial mass function. We show that there exists a strong degeneracy between the viewing angle and the dispersion of the RC absolute magnitude distribution. The value of the latter is strongly dependent on the assumptions made about the intrinsic luminosity function of the bulge

Removal of ecotoxicity of 17α-ethinylestradiol using TAML/peroxide water treatment

17α -ethinylestradiol (EE2), a synthetic oestrogen in oral contraceptives, is one of many pharmaceuticals found in inland waterways worldwide as a result of human consumption and excretion into wastewater treatment systems. At low parts per trillion (ppt), EE2 induces feminisation of male fish, diminishing reproductive success and causing fish population collapse. Intended water quality standards for EE2 set a much needed global precedent. Ozone and activated carbon provide effective wastewater treatments, but their energy intensities and capital/operating costs are formidable barriers to adoption. Here we describe the technical and environmental performance of a fast- developing contender for mitigation of EE2 contamination of wastewater based upon smallmolecule, full-functional peroxidase enzyme replicas called “TAML activators”. From neutral to basic pH, TAML activators with H2O2 efficiently degrade EE2 in pure lab water, municipal effluents and EE2-spiked synthetic urine. TAML/H2O2 treatment curtails estrogenicity in vitro and substantially diminishes fish feminization in vivo. Our results provide a starting point for a future process in which tens of thousands of tonnes of wastewater could be treated per kilogram of catalyst. We suggest TAML/H2O2 is a worthy candidate for exploration as an environmentally compatible, versatile, method for removing EE2 and other pharmaceuticals from municipal wastewaters.Heinz Endowments, the Swiss National Science Foundation, the Steinbrenner Institute for a Steinbrenner Doctoral Fellowship. NMR instrumentation at CMU was partially supported by NSF (CHE-0130903 and CHE-1039870)

New type of microengine using internal combustion of hydrogen and oxygen

Microsystems become part of everyday life but their application is restricted by lack of strong and fast motors (actuators) converting energy into motion. For example, widespread internal combustion engines cannot be scaled down because combustion reactions are quenched in a small space. Here we present an actuator with the dimensions 100x100x5 um^3 that is using internal combustion of hydrogen and oxygen as part of its working cycle. Water electrolysis driven by short voltage pulses creates an extra pressure of 0.5-4 bar for a time of 100-400 us in a chamber closed by a flexible membrane. When the pulses are switched off this pressure is released even faster allowing production of mechanical work in short cycles. We provide arguments that this unexpectedly fast pressure decrease is due to spontaneous combustion of the gases in the chamber. This actuator is the first step to truly microscopic combustion engines.Comment: Paper and Supplementary Information (to appear in Scientific Reports

A momentum-dependent perspective on quasiparticle interference in Bi_{2}Sr_{2}CaCu_{2}O_{8+\delta}

Angle Resolved Photoemission Spectroscopy (ARPES) probes the momentum-space electronic structure of materials, and provides invaluable information about the high-temperature superconducting cuprates. Likewise, the cuprate real-space, inhomogeneous electronic structure is elucidated by Scanning Tunneling Spectroscopy (STS). Recently, STS has exploited quasiparticle interference (QPI) - wave-like electrons scattering off impurities to produce periodic interference patterns - to infer properties of the QP in momentum-space. Surprisingly, some interference peaks in Bi_{2}Sr_{2}CaCu_{2}O_{8+\delta} (Bi-2212) are absent beyond the antiferromagnetic (AF) zone boundary, implying the dominance of particular scattering process. Here, we show that ARPES sees no evidence of quasiparticle (QP) extinction: QP-like peaks are measured everywhere on the Fermi surface, evolving smoothly across the AF zone boundary. This apparent contradiction stems from different natures of single-particle (ARPES) and two-particle (STS) processes underlying these probes. Using a simple model, we demonstrate extinction of QPI without implying the loss of QP beyond the AF zone boundary

Evaluation of Phage Display Discovered Peptides as Ligands for Prostate-Specific Membrane Antigen (PSMA)

The aim of this study was to identify potential ligands of PSMA suitable for further development as novel PSMA-targeted peptides using phage display technology. The human PSMA protein was immobilized as a target followed by incubation with a 15-mer phage display random peptide library. After one round of prescreening and two rounds of screening, high-stringency screening at the third round of panning was performed to identify the highest affinity binders. Phages which had a specific binding activity to PSMA in human prostate cancer cells were isolated and the DNA corresponding to the 15-mers were sequenced to provide three consensus sequences: GDHSPFT, SHFSVGS and EVPRLSLLAVFL as well as other sequences that did not display consensus. Two of the peptide sequences deduced from DNA sequencing of binding phages, SHSFSVGSGDHSPFT and GRFLTGGTGRLLRIS were labeled with 5-carboxyfluorescein and shown to bind and co-internalize with PSMA on human prostate cancer cells by fluorescence microscopy. The high stringency requirements yielded peptides with affinities KD∼1 μM or greater which are suitable starting points for affinity maturation. While these values were less than anticipated, the high stringency did yield peptide sequences that apparently bound to different surfaces on PSMA. These peptide sequences could be the basis for further development of peptides for prostate cancer tumor imaging and therapy. © 2013 Shen et al

Graphene-based photovoltaic cells for near-field thermal energy conversion

Thermophotovoltaic devices are energy-conversion systems generating an electric current from the thermal photons radiated by a hot body. In far field, the efficiency of these systems is limited by the thermodynamic Schockley-Queisser limit corresponding to the case where the source is a black body. On the other hand, in near field, the heat flux which can be transferred to a photovoltaic cell can be several orders of magnitude larger because of the contribution of evanescent photons. This is particularly true when the source supports surface polaritons. Unfortunately, in the infrared where these systems operate, the mismatch between the surface-mode frequency and the semiconductor gap reduces drastically the potential of this technology. Here we show that graphene-based hybrid photovoltaic cells can significantly enhance the generated power paving the way to a promising technology for an intensive production of electricity from waste heat.Comment: 5 pages, 4 figure

Controlling Cherenkov angles with resonance transition radiation

Cherenkov radiation provides a valuable way to identify high energy particles in a wide momentum range, through the relation between the particle velocity and the Cherenkov angle. However, since the Cherenkov angle depends only on material's permittivity, the material unavoidably sets a fundamental limit to the momentum coverage and sensitivity of Cherenkov detectors. For example, Ring Imaging Cherenkov detectors must employ materials transparent to the frequency of interest as well as possessing permittivities close to unity to identify particles in the multi GeV range, and thus are often limited to large gas chambers. It would be extremely important albeit challenging to lift this fundamental limit and control Cherenkov angles as preferred. Here we propose a new mechanism that uses constructive interference of resonance transition radiation from photonic crystals to generate both forward and backward Cherenkov radiation. This mechanism can control Cherenkov angles in a flexible way with high sensitivity to any desired range of velocities. Photonic crystals thus overcome the severe material limit for Cherenkov detectors, enabling the use of transparent materials with arbitrary values of permittivity, and provide a promising option suited for identification of particles at high energy with enhanced sensitivity.Comment: There are 16 pages and 4 figures for the manuscript. Supplementary information with 18 pages and 5 figures, appended at the end of the file with the manuscript. Source files in Word format converted to PDF. Submitted to Nature Physic

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events