How galactic environment regulates star formation

In a new simple model I reconcile two contradictory views on the factors that determine the rate at which molecular clouds form stars-internal structure versus external, environmental influences-providing a unified picture for the regulation of star formation in galaxies. In the presence of external pressure, the pressure gradient set up within a self-gravitating turbulent (isothermal) cloud leads to a non-uniform density distribution. Thus the local environment of a cloud influences its internal structure. In the simple equilibrium model, the fraction of gas at high density in the cloud interior is determined simply by the cloud surface density, which is itself inherited from the pressure in the immediate surroundings. This idea is tested using measurements of the properties of local clouds, which are found to show remarkable agreement with the simple equilibrium model. The model also naturally predicts the star formation relation observed on cloud scales and at the same time provides a mapping between this relation and the closer-to-linear molecular star formation relation measured on larger scales in galaxies. The key is that pressure regulates not only the molecular content of the ISM but also the cloud surface density. I provide a straightforward prescription for the pressure regulation of star formation that can be directly implemented in numerical models. Predictions for the dense gas fraction and star formation efficiency measured on large-scales within galaxies are also presented, establishing the basis for a new picture of star formation regulated by galactic environment

Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.Comment: ISMIR 201

The Effect of Psychiatric Rehabilitation on the Activity and Participation Level of Clients with Long-Term Psychiatric Disabilities

During the last decades of the 20th century, many psychiatric hospitals changed the living environments of their clients with long-term psychiatric disabilities. We investigated the effect of this environmental psychiatric rehabilitation and normalization process on the activity and participation level of such clients residing in one Dutch psychiatric hospital. The seven years of panel research demonstrated that more normal living environments have a positive effect on clients’ activity and participation level. This is controlled for the fact that younger clients, and clients with a relative high activity and participation level were selected for these normal living environments.

Automated tracking of colloidal clusters with sub-pixel accuracy and precision

Quantitative tracking of features from video images is a basic technique employed in many areas of science. Here, we present a method for the tracking of features that partially overlap, in order to be able to track so-called colloidal molecules. Our approach implements two improvements into existing particle tracking algorithms. Firstly, we use the history of previously identified feature locations to successfully find their positions in consecutive frames. Secondly, we present a framework for non-linear least-squares fitting to summed radial model functions and analyze the accuracy (bias) and precision (random error) of the method on artificial data. We find that our tracking algorithm correctly identifies overlapping features with an accuracy below 0.2% of the feature radius and a precision of 0.1 to 0.01 pixels for a typical image of a colloidal cluster. Finally, we use our method to extract the three-dimensional diffusion tensor from the Brownian motion of colloidal dimers.Comment: 20 pages, 8 figures. Non-revised preprint version, please refer to http://dx.doi.org/10.1088/1361-648X/29/4/04400

Propfan noise propagation

The unconventional supersonic tip speed of advanced propellers has led to uncertainties about Propfan's noise acceptability and compliance with Federal Aviation Noise Regulation (FAR 36). Overhead flight testing of the Propfan with an SR-7L blade during 1989's Propfan Test Assessment (PTA) Program have shown unexpectedly high far-field sound pressure levels. This study here attempts to provide insights into the acoustics of a single-rotating propeller (SRP) with supersonic tip speed. At the same time, the role of the atmosphere in shaping the far-field noise characteristics is investigated

Customer flow, intermediaries, and the discovery of the equilibrium riskfree rate

Macro announcements change the equilibrium riskfree rate. We find that treasury prices reflect part of the impact instantaneously, but intermediaries rely on their customer order flow in the 15 minutes after the announcement to discover the full impact. We show that this customer flow informativeness is strongest at times when analyst forecasts of macro variables are highly dispersed. We study 30 year treasury futures to identify the customer flow. We further show that intermediaries appear to benefit from privately recognizing informed customer flow, as, in the cross-section, their own-account trade profitability correlates with access to customer orders, controlling for volatility, competition, and the announcement surprise. These results suggest that intermediaries learn about equilibrium riskfree rates through customer orders

Microparticle assembly pathways on lipid membranes

Understanding interactions between microparticles and lipid membranes is of increasing importance, especially for unraveling the influence of microplastics on our health and environment. Here, we study how a short-ranged adhesive force between microparticles and model lipid membranes causes membrane-mediated particle assembly. Using confocal microscopy, we observe the initial particle attachment to the membrane, then particle wrapping, and in rare cases spontaneous membrane tubulation. In the attached state, we measure that the particle mobility decreases by 26%. If multiple particles adhere to the same vesicle, their initial single-particle state determines their interactions and subsequent assembly pathways: 1) attached particles only aggregate when small adhesive vesicles are present in solution, 2) wrapped particles reversibly attract one another by membrane deformation, and 3) a combination of wrapped and attached particles form membrane-mediated dimers, which further assemble into a variety of complex structures. The experimental observation of distinct assembly pathways induced only by a short ranged membrane-particle adhesion, shows that a cellular cytoskeleton or other active components are not required for microparticle aggregation. We suggest that this membrane-mediated microparticle aggregation is a reason behind reported long retention times of polymer microparticles in organisms.Comment: 20 pages, 11 figures (including supporting material

The Relation between Galaxy Structure and Spectral Type: Implications for the Buildup of the Quiescent Galaxy Population at 0.5<z<2.0

We present the relation between galaxy structure and spectral type, using a K-selected galaxy sample at 0.5<z<2.0. Based on similarities between the UV-to-NIR spectral energy distributions, we classify galaxies into 32 spectral types. The different types span a wide range in evolutionary phases, and thus -- in combination with available CANDELS/F160W imaging -- are ideal to study the structural evolution of galaxies. Effective radii (R_e) and Sersic parameters (n) have been measured for 572 individual galaxies, and for each type, we determine R_e at fixed stellar mass by correcting for the mass-size relation. We use the rest-frame U-V vs. V-J diagram to investigate evolutionary trends. When moving into the direction perpendicular to the star-forming sequence, in which we see the Halpha equivalent width and the specific star formation rate (sSFR) decrease, we find a decrease in R_e and an increase in n. On the quiescent sequence we find an opposite trend, with older redder galaxies being larger. When splitting the sample into redshift bins, we find that young post-starburst galaxies are most prevalent at z>1.5 and significantly smaller than all other galaxy types at the same redshift. This result suggests that the suppression of star formation may be associated with significant structural evolution at z>1.5. At z<1, galaxy types with intermediate sSFRs (10^{-11.5}-10^{-10.5} yr^-1) do not have post-starburst SED shapes. These galaxies have similar sizes as older quiescent galaxies, implying that they can passively evolve onto the quiescent sequence, without increasing the average size of the quiescent galaxy population.Comment: 7 pages, 5 figures; Accepted for publication in ApJ

A constant limiting mass scale for flat early-type galaxies from z=1 to z=0: density evolves but shapes do not

We measure the evolution in the intrinsic shape distribution of early-type galaxies from z~1 to z~0 by analyzing their projected axis-ratio distributions. We extract a low-redshift sample (0.04 < z < 0.08) of early-type galaxies with very low star-formation rates from the SDSS, based on a color-color selection scheme and verified through the absence of emission lines in the spectra. The inferred intrinsic shape distribution of these early-type galaxies is strongly mass dependent: the typical short-to-long intrinsic axis-ratio of high-mass early-type galaxies (>1e11 M_sun) is 2:3, where as at masses below 1e11 M_sun this ratio narrows to 1:3, or more flattened galaxies. In an entirely analogous manner we select a high-redshift sample (0.6 < z < 0.8) from two deep-field surveys: GEMS and COSMOS. We find a seemingly universal mass of ~1e11 M_sun for highly flatted early-type systems at all redshifts. This implies that the process that grows an early-type galaxy above this ceiling mass involves forming round systems. Using both parametric and non-parametric tests, we find no evolution in the projected axis-ratio distribution for galaxies with masses >3e10 M_sun with redshift. At the same time, our samples imply an increase of 2-3x in co-moving number density for early-type galaxies at masses >3e10 M_sun, in agreement with previous studies. Given the direct connection between the axis-ratio distribution and the underlying bulge-to-disk ratio distribution, our findings imply that the number density evolution of early-type galaxies is not exclusively driven by the emergence of either bulge- or disk-dominated galaxies, but rather by a balanced mix that depends only on the stellar mass of the galaxy. The challenge for galaxy formation models is to reproduce this overall non-evolving ratio of flattened to round early-type galaxies in the context of a continually growing population.Comment: 14 pages in emulate ApJ format, 8 color figures, submitted to ApJ, comments welcome, fixed missing reference