Cosmology with the lights off: Standard sirens in the Einstein Telescope era

We explore the prospects for constraining cosmology using gravitational-wave (GW) observations of neutron-star binaries by the proposed Einstein Telescope (ET), exploiting the narrowness of the neutron-star mass function. Double neutron-star (DNS) binaries are expected to be one of the first sources detected after "first-light" of Advanced LIGO and are expected to be detected at a rate of a few tens per year in the advanced era. However the proposed ET could catalog tens of thousands per year. Combining the measured source redshift distributions with GW-network distance determinations will permit not only the precision measurement of background cosmological parameters, but will provide an insight into the astrophysical properties of these DNS systems. Of particular interest will be to probe the distribution of delay times between DNS-binary creation and subsequent merger, as well as the evolution of the star-formation rate density within ET's detection horizon. Keeping H_0, \Omega_{m,0} and \Omega_{\Lambda,0} fixed and investigating the precision with which the dark-energy equation-of-state parameters could be recovered, we found that with 10^5 detected DNS binaries we could constrain these parameters to an accuracy similar to forecasted constraints from future CMB+BAO+SNIa measurements. Furthermore, modeling the merger delay-time distribution as a power-law, and the star-formation rate (SFR) density as a parametrized version of the Porciani and Madau SF2 model, we find that the associated astrophysical parameters are constrained to within ~ 10%. All parameter precisions scaled as 1/sqrt(N), where N is the number of cataloged detections. We also investigated how precisions varied with the intrinsic underlying properties of the Universe and with the distance reach of the network (which may be affected by the low-frequency cutoff of the detector).Comment: 24 pages, 11 figures, 6 tables. Minor changes to reflect published version. References updated and correcte

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.Comment: 18 pages, 6 figures, 3 table

A comparison of CMB- and HLA-based approaches to type I interoperability reference model problems for COTS-based distributed simulation

Commercial-off-the-shelf (COTS) simulation packages (CSPs) are software used by many simulation modellers to build and experiment with models of various systems in domains such as manufacturing, health, logistics and commerce. COTS distributed simulation deals with the interoperation of CSPs and their models. Such interoperability has been classified into six interoperability reference models. As part of an on-going standardisation effort, this paper introduces the COTS Simulation Package Emulator, a proposed benchmark that can be used to investigate Type I interoperability problems in COTS distributed simulation. To demonstrate its use, two approaches to this form of interoperability are discussed, an implementation of the CMB conservative algorithm, an example of a so-called “light” approach, and an implementation of the HLA TAR algorithm, an example of a so-called “heavy” approach. Results from experimentation over four federation topologies are presented and it is shown the HLA approach out performs the CMB approach in almost all cases. The paper concludes that the CSPE benchmark is a valid basis from which the most efficient approach to Type I interoperability problems for COTS distributed simulation can be discovered

Inactivating the spindle checkpoint kinase Bub1 during embryonic development results in a global shutdown of proliferation

<p>Abstract</p> <p>Background</p> <p>Bub1 is a component of the spindle assembly checkpoint, a surveillance mechanism that maintains chromosome stability during M-phase. Bub1 is essential during the early stages of embryogenesis, with homozygous <it>BUB1</it>-null mice dying shortly after day E3.5. Bub1 is also required later during embryogenesis; inactivation of <it>BUB1 </it>on day E10.5 appears to rapidly block all further development. However, the mechanism(s) responsible for this phenotype remain unclear.</p> <p>Findings</p> <p>Here we show that inactivating <it>BUB1 </it>on day E10.5 stalls embryogenesis within 48 hours. This is accompanied by a global shutdown of proliferation, widespread apoptosis and haemorrhaging.</p> <p>Conclusion</p> <p>Our results suggest that Bub1 is required throughout the developing embryo for cellular proliferation. Therefore, Bub1 has been shown to be essential in all scenarios analyzed thus far in mice: proliferation of cultured fibroblasts, spermatogenesis, oogenesis and both early and late embryonic development. This likely reflects the fact that Bub1 has dual functions during mitosis, being required for both SAC function and chromosome alignment.</p

Ecophysiological traits of grasses: resolving the effects of photosynthetic pathway and phylogeny

C4 photosynthesis is an important example of convergent evolution in plants, having arisen in eudicots, monocots and diatoms. Comparisons between such diverse groups are confounded by phylogenetic and ecological differences, so that only broad generalisations can be made about the role of C4 photosynthesis in&#xd;&#xa;determining ecophysiological traits. However, 60% of C4 species occur in the grasses (Poaceae) and molecular phylogenetic techniques confirm that there are between 8 and 17 independent origins of C4 photosynthesis in the Poaceae. In a screening experiment, we compared leaf physiology and growth traits across several major&#xd;&#xa;independent C3 &#x26; C4 groups within the Poaceae, asking 1) which traits differ consistently between photosynthetic&#xd;&#xa;types and 2) which traits differ consistently between clades within each photosynthetic type

A microfluidic processor for gene expression profiling of single human embryonic stem cells

The gene expression of human embryonic stem cells (hESC) is a critical aspect for understanding the normal and pathological development of human cells and tissues. Current bulk gene expression assays rely on RNA extracted from cell and tissue samples with various degree of cellular heterogeneity. These cell population averaging data are difficult to interpret, especially for the purpose of understanding the regulatory relationship of genes in the earliest phases of development and differentiation of individual cells. Here, we report a microfluidic approach that can extract total mRNA from individual single-cells and synthesize cDNA on the same device with high mRNA-to-cDNA efficiency. This feature makes large-scale single-cell gene expression profiling possible. Using this microfluidic device, we measured the absolute numbers of mRNA molecules of three genes (B2M, Nodal and Fzd4) in a single hESC. Our results indicate that gene expression data measured from cDNA of a cell population is not a good representation of the expression levels in individual single cells. Within the G0/G1 phase pluripotent hESC population, some individual cells did not express all of the 3 interrogated genes in detectable levels. Consequently, the relative expression levels, which are broadly used in gene expression studies, are very different between measurements from population cDNA and single-cell cDNA. The results underscore the importance of discrete single-cell analysis, and the advantages of a microfluidic approach in stem cell gene expression studies

The Spindle Assembly Checkpoint

During mitosis and meiosis, the spindle assembly checkpoint acts to maintain genome stability by delaying cell division until accurate chromosome segregation can be guaranteed. Accuracy requires that chromosomes become correctly attached to the microtubule spindle apparatus via their kinetochores. When not correctly attached to the spindle, kinetochores activate the spindle assembly checkpoint network, which in turn blocks cell cycle progression. Once all kinetochores become stably attached to the spindle, the checkpoint is inactivated, which alleviates the cell cycle block and thus allows chromosome segregation and cell division to proceed. Here we review recent progress in our understanding of how the checkpoint signal is generated, how it blocks cell cycle progression and how it is extinguished

A physical model for low-frequency electromagnetic induction in the near field based on direct interaction between transmitter and receiver electrons

A physical model of electromagnetic induction is developed which relates directly the forces between electrons in the transmitter and receiver windings of concentric coaxial finite coils in the near-field region. By applying the principle of superposition, the contributions from accelerating electrons in successive current loops are summed, allowing the peak-induced voltage in the receiver to be accurately predicted. Results show good agreement between theory and experiment for various receivers of different radii up to five times that of the transmitter. The limitations of the linear theory of electromagnetic induction are discussed in terms of the non-uniform current distribution caused by the skin effect. In particular, the explanation in terms of electromagnetic energy and Poynting’s theorem is contrasted with a more direct explanation based on variable filament induction across the conductor cross section. As the direct physical model developed herein deals only with forces between discrete current elements, it can be readily adapted to suit different coil geometries and is widely applicable in various fields of research such as near-field communications, antenna design, wireless power transfer, sensor applications and beyond