Temperature dependence of photoluminescence from single and ensemble InAs/GaAs quantum dots

We investigate the temperature dependence of photoluminescence from single and ensemble InAs/GaAs quantum dots systematically. As temperature increases, the exciton emission peak for single quantum dot shows broadening and redshift. For ensemble quantum dots, however, the exciton emission peak shows narrowing and fast redshift. We use a simple steady-state rate equation model to simulate the experimental data of photoluminescence spectra. It is confirmed that carrier-phonon scattering gives the broadening of the exciton emission peak in single quantum dots while the effects of carrier thermal escape and retrapping play an important role in the narrowing and fast redshift of the exciton emission peak in ensemble quantum dots.

Material transport in self-assembled InAs/GaAs quantum dot ensemble

Introducing the growth interruption between the InAs deposition and subsequent GaAs growth in self-assembled quantum dot (QD) structures, the material transport process in the InAs layers has been investigated by photoluminescence and transmission electron microscopy measurement. InAs material in structures without misfit dislocations transfers from the wetting layer to QDs corresponding to the red-shift of PL peak energy due to interruption. On the other hand, the PL peak shifts to higher energy in the structures with dislocations. In this case, the misfit dislocations would capture the InAs material from the surrounding wetting layer and coherent islands leading to the reduction of the size of these QDs. The variations in the PL intensity and Linewidth are also discussed.

Material transport in self-assembled InAs/GaAs quantum dot ensemble

Introducing the growth interruption between the InAs deposition and subsequent GaAs growth in self-assembled quantum dot (QD) structures, the material transport process in the InAs layers has been investigated by photoluminescence and transmission electron microscopy measurement. InAs material in structures without misfit dislocations transfers from the wetting layer to QDs corresponding to the red-shift of PL peak energy due to interruption. On the other hand, the PL peak shifts to higher energy in the structures with dislocations. In this case, the misfit dislocations would capture the InAs material from the surrounding wetting layer and coherent islands leading to the reduction of the size of these QDs. The variations in the PL intensity and Linewidth are also discussed.

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.

Bridging the Divide Between the Soloist and the Ensemble Player

In conservatories and music schools, the general practice for an aspiring pianist is to focus on solo performance learning mainly solo repertoire. With the advent of the advanced degree in collaborative piano, pianists could submerge themselves in the study of duo sonatas, larger chamber music ensembles, and art song. The appearance of this degree was an important step in the development of pianists, as this kind of work requires specific training and focus to master the vast repertoire involved. However it also more clearly brought out the invisible divide separating the solo pianist from the collaborative pianist, a.k.a. the accompanist. While geniuses such as Bach, Beethoven and Brahms were known to compose and perform all types of music, the appearance of super stars such as Liszt and Paganini helped bring into being the term accompanist and since then music world has tacitly embraced this divide. The goal of my dissertational study is to show that this divide need not exist. The three recitals which comprise this dissertational project were all performed at the University of Maryland, the first on 12 November 2010 at Gildenhom Recital Hall, the second at Ulrich Recital Hall on 10 September 2011, and the third at Gildenhorn Recital Hall on 11 November 2011. The repertoire included Rachmaninoff Prelude in g# minor op. 32 no. 12 and Etude-Tableaux in Eb minor op. 29 no. 5, Brahms Sonata for Piano and Violin in d minor op. 108, Mendelssohn Piano Trio in d minor op. 49, Chopin Sonata No.2 in Bb minor, Franck Sonata for Piano and Violin, Prokofiev Piano Concerto no. 2 in g minor op. 16 with pianist Elizabeth Brown as orchestra, Beethoven Sonata for Piano and Violin in A op 47 (Kreutzer), and Paul Schoenfield Cafe Music. All works with violin and cello were performed with violinist Rebecca Racusin, and cellist Devree Lewis. The recitals were recorded on compact discs and are archived within the Digital Repository at the University of Maryland(DRUM).

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

CCD photometry of distant comets. III - Ensemble properties of Jupiter-family comets

We describe the results of a ground-based observational "snapshot" study of Jupiter-family comets in the heliocentric range 2.29 AU less than or equal to R-h less than or equal to 5.72 AU. Results are presented based on observations from the 1m JKT on the island of La Palma. A total of 25 comets were targeted with 15 being positively detected. Broad-band VRI photometry was performed to determine dimensions, colour indices, and dust production rates in terms of the "A frho" formalism. The results for selected comets are compared with previous investigations. Ensemble properties of the Jupiter- family population have been investigated by combining the results presented here with those of Lowry et al. (1999), and Lowry & Fitzsimmons (2001). We find that the cumulative size distribution of the Jupiter-family comets can be described by a power law of the form; Sigma(> r) proportional to r(-1.6+/- 0.1). This size distribution is considerably shallower than that found for the observed Edgeworth-Kuiper belt objects, which may reflect either an intrinsic difference at small km- sizes in the belt, or the various processes affecting the nuclei of comets as their orbits evolve from the Edgeworth- Kuiper belt to the inner Solar system. Also, there would appear to be no correlation between nuclear absolute magnitude and perihelion distance. Finally, for the sample of active comets, there is a distinct correlation between absolute R band magnitude and perihelion distance, which can be explained by either a discovery bias towards brighter comets or in terms of "rubble" mantle formation.

Mapping the evolution of optically generated rotational wave packets in a room-temperature ensemble of D2

A coherent superposition of rotational states in D2 has been excited by nonresonant, ultrafast (12 fs), intense (2×1014 W cm-2) 800 nm laser pulses, leading to impulsive dynamic alignment. Field-free evolution of this rotational wave packet has been mapped to high temporal resolution by a time-delayed pulse, initiating rapid double ionization, which is highly sensitive to the angle of orientation of the molecular axis with respect to the polarization direction, . The detailed fractional revivals of the neutral D2 wave packet as a function of and evolution time have been observed and modeled theoretically.

Revealing differences in gene network inference algorithms on the network level by ensemble methods

Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.

Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.