Cathodoluminescence and electron microscopy of red quantum dots used for display applications

Cathodoluminescent imaging of the visible light emitted from quantum dots is reported. The shape and uniformity of individual particles is observed in the STEM electron image and the image of the particles created from their visible light collected simultaneously is shown. Visible light images of the 13nm sized particles are reported for clusters of particles. Emission spectra collected from a small clusters of QDs are also reported

Partitioning Complex Networks via Size-constrained Clustering

The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferring the solution to the next finer graph and applying a local search algorithm to improve the current solution. In this paper, we describe a novel approach to partition graphs effectively especially if the networks have a highly irregular structure. More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm. The same algorithm that provides the size-constrained clusterings can also be used during uncoarsening as a fast and simple local search algorithm. Depending on the algorithm's configuration, we are able to compute partitions of very high quality outperforming all competitors, or partitions that are comparable to the best competitor in terms of quality, hMetis, while being nearly an order of magnitude faster on average. The fastest configuration partitions the largest graph available to us with 3.3 billion edges using a single machine in about ten minutes while cutting less than half of the edges than the fastest competitor, kMetis

Candidate LBV stars in galaxy NGC 7793 found via HST photometry + MUSE spectroscopy

Only about 19 Galactic and 25 extragalactic bonafide luminous blue variables (LBVs) are known to date. This incomplete census prevents our understanding of this crucial phase of massive star evolution which leads to the formation of heavy binary black holes via the classical channel. With large samples of LBVs one could better determine the duration and maximum stellar luminosity which characterize this phase. We search for candidate LBVs (cLBVs) in a new galaxy, NGC 7793. For this purpose, we combine high spatial resolution images from two Hubble Space Telescope (HST) programs with optical spectroscopy from the Multi Unit Spectroscopic Explorer (MUSE). By combining PSF-fitting photometry measured on F547M, F657N, and F814W images, with restrictions on point-like appearance (at HST resolution) and H α luminosity, we find 100 potential cLBVs, 36 of which fall in the MUSE fields. Five of the latter 36 sources are promising cLBVs which have MV ≤ −7 and a combination of: H α with a P-Cygni profile; no [O I]λ6300 emission; weak or no [O III]λ5007 emission; large [N II]/H α relative to H II regions; and [S II]λ6716/[S II]λ6731∼1⁠. It is not clear if these five cLBVs are isolated from O-type stars, which would favour the binary formation scenario of LBVs. Our study, which approximately covers one fourth of the optical disc of NGC 7793, demonstrates how by combining the above HST surveys with multi-object spectroscopy from 8-m class telescopes, one can efficiently find large samples of cLBVs in nearby galaxies

On the Complexity of Local Distributed Graph Problems

This paper is centered on the complexity of graph problems in the well-studied LOCAL model of distributed computing, introduced by Linial [FOCS '87]. It is widely known that for many of the classic distributed graph problems (including maximal independent set (MIS) and $(\Delta+1)$-vertex coloring), the randomized complexity is at most polylogarithmic in the size $n$ of the network, while the best deterministic complexity is typically $2^{O(\sqrt{\log n})}$. Understanding and narrowing down this exponential gap is considered to be one of the central long-standing open questions in the area of distributed graph algorithms. We investigate the problem by introducing a complexity-theoretic framework that allows us to shed some light on the role of randomness in the LOCAL model. We define the SLOCAL model as a sequential version of the LOCAL model. Our framework allows us to prove completeness results with respect to the class of problems which can be solved efficiently in the SLOCAL model, implying that if any of the complete problems can be solved deterministically in $\log^{O(1)} n$ rounds in the LOCAL model, we can deterministically solve all efficient SLOCAL-problems (including MIS and $(\Delta+1)$-coloring) in $\log^{O(1)} n$ rounds in the LOCAL model. We show that a rather rudimentary looking graph coloring problem is complete in the above sense: Color the nodes of a graph with colors red and blue such that each node of sufficiently large polylogarithmic degree has at least one neighbor of each color. The problem admits a trivial zero-round randomized solution. The result can be viewed as showing that the only obstacle to getting efficient determinstic algorithms in the LOCAL model is an efficient algorithm to approximately round fractional values into integer values

Long-range mechanical force in colony branching and tumor invasion

The most concerned factors for cancer prognosis are tumor invasion and metastasis. The patterns of tumor invasion can be characterized as random infiltration to surrounding extracellular matrix (ECM) or formation of long-range path for collective migration. Recent studies indicate that mechanical force plays an important role in tumor infiltration and collective migration. However, how tumor colonies develop mechanical interactions with each other to initiate various invasion patterns is unclear. Using a micro-patterning technique, we partition cells into clusters to mimic tumor colonies and quantitatively induce colony-ECM interactions. We find that pre-malignant epithelial cells, in response to concentrations of type I collagen in ECM ([COL]), develop various branching patterns resembling those observed in tumor invasion. In contrast with conventional thought, these patterns require long-range (~ 600 μm) transmission of traction force, but not biochemical factors. At low [COL], cell colonies synergistically develop pairwise and directed branching mimicking the formation of long-range path. By contrast, at high [COL] or high colony density, cell colonies develop random branching and scattering patterns independent of each other. Our results suggest that tumor colonies might select different invasive patterns depending on their interactions with each other and with the ECM

The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS