Correlation between the Mean Matter Density and the Width of the Saturated Lyman Alpha Absorption

We report a scaling of the mean matter density with the width of the saturated Lyman alpha absorptions. This property is established using the ``pseudo-hydro'' technique (Croft et al. 1998). It provides a constraint for the inversion of the Lyman alpha forest, which encounters difficulty in the saturated region. With a Gaussian density profile and the scaling relation, a simple inversion of the simulated Lyman alpha forests shows that the one-dimensional mass power spectrum is well recovered on scales above 2 Mpc/h, or roughly k < 0.03 s/km, at z=3. The recovery underestimates the power on small scales, but improvement is possible with a more sophisticated algorithm.Comment: 7 pages, 9 figures, accepted for publication in MNRAS, replaced by the version after proo

On the Three-dimensional Lattice Model

Using the restricted star-triangle relation, it is shown that the $N$-state spin integrable model on a three-dimensional lattice with spins interacting round each elementary cube of the lattice proposed by Mangazeev, Sergeev and Stroganov is a particular case of the Bazhanov-Baxter model.Comment: 8 pages, latex, 4 figure

Measuring Baryon Acoustic Oscillations with Millions of Supernovae

Since type Ia Supernovae (SNe) explode in galaxies, they can, in principle, be used as the same tracer of the large-scale structure as their hosts to measure baryon acoustic oscillations (BAOs). To realize this, one must obtain a dense integrated sampling of SNe over a large fraction of the sky, which may only be achievable photometrically with future projects such as the Large Synoptic Survey Telescope. The advantage of SN BAOs is that SNe have more uniform luminosities and more accurate photometric redshifts than galaxies, but the disadvantage is that they are transitory and hard to obtain in large number at high redshift. We find that a half-sky photometric SN survey to redshift z = 0.8 is able to measure the baryon signature in the SN spatial power spectrum. Although dark energy constraints from SN BAOs are weak, they can significantly improve the results from SN luminosity distances of the same data, and the combination of the two is no longer sensitive to cosmic microwave background priors.Comment: 4 pages, 3 figures, ApJL accepte

One-point Statistics of the Cosmic Density Field in Real and Redshift Spaces with A Multiresolutional Decomposition

In this paper, we develop a method of performing the one-point statistics of a perturbed density field with a multiresolutional decomposition based on the discrete wavelet transform (DWT). We establish the algorithm of the one-point variable and its moments in considering the effects of Poisson sampling and selection function. We also establish the mapping between the DWT one-point statistics in redshift space and real space, i.e. the algorithm for recovering the DWT one-point statistics from the redshift distortion of bulk velocity, velocity dispersion, and selection function. Numerical tests on N-body simulation samples show that this algorithm works well on scales from a few hundreds to a few Mpc/h for four popular cold dark matter models. Taking the advantage that the DWT one-point variable is dependent on both the scale and the shape (configuration) of decomposition modes, one can design estimators of the redshift distortion parameter (beta) from combinations of DWT modes. When the non-linear redshift distortion is not negligible, the beta estimator from quadrupole-to-monopole ratio is a function of scale. This estimator would not work without adding information about the scale-dependence, such as the power-spectrum index or the real-space correlation function of the random field. The DWT beta estimators, however, do not need such extra information. Numerical tests show that the proposed DWT estimators are able to determine beta robustly with less than 15% uncertainty in the redshift range 0 < z < 3.Comment: 39 pages, 12 figures, ApJ accepte

Computation Offloading in Multi-access Edge Computing using Deep Sequential Model based on Reinforcement Learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Multi-access Edge Computing (MEC) is an emerging paradigm which utilizes computing resources at the network edge to deploy heterogeneous applications and services. In the MEC system, mobile users and enterprises can offload computation-intensive tasks to nearby computing resources to reduce latency and save energy. When users make offloading decisions, the task dependency needs to be considered. Due to the NP-hardness of the offloading problem, the existing solutions are mainly heuristic, and therefore have difficulties in adapting to the increasingly complex and dynamic applications. To address the challenges of task dependency and adapting to dynamic scenarios, we propose a new Deep Reinforcement Learning (DRL) based offloading framework, which can efficiently learn the offloading policy uniquely represented by a specially designed Sequence-to-Sequence (S2S) neural network. The proposed DRL solution can automatically discover the common patterns behind various applications so as to infer an optimal offloading policy in different scenarios. Simulation experiments were conducted to evaluate the performance of the proposed DRL-based method with different data transmission rates and task numbers. The results show that our method outperforms two heuristic baselines and achieves nearly optimal performance.Engineering and Physical Sciences Research Council (EPSRC

An independent test of the photometric selection of white dwarf candidates using LAMOST DR3

In Gentile Fusillo et al. (2015) we developed a selection method for white dwarf candidates which makes use of photometry, colours and proper motions to calculate a probability of being a white dwarf (Pwd). The application of our method to the Sloan Digital Sky Survey (SDSS) data release 10 resulted in nearly 66,000 photometrically selected objects with a derived Pwd, approximately 21000 of which are high confidence white dwarf candidates. Here we present an independent test of our selection method based on a sample of spectroscopically confirmed white dwarfs from the LAMOST (Large Sky Area Multi-Fiber Spectroscopic Telescope) survey. We do this by cross matching all our $\sim$66,000 SDSS photometric white dwarf candidates with the over 4 million spectra available in the third data release of LAMOST. This results in 1673 white dwarf candidates with no previous SDSS spectroscopy, but with available LAMOST spectra. Among these objects we identify 309 genuine white dwarfs. We find that our Pwd can efficiently discriminate between confirmed LAMOST white dwarfs and contaminants. Our white dwarf candidate selection method can be applied to any multi-band photometric survey and in this work we conclusively confirm its reliability in selecting white dwarfs without recourse to spectroscopy. We also discuss the spectroscopic completeness of white dwarfs in LAMOST, as well as deriving effective temperatures, surface gravities and masses for the hydrogen-rich atmosphere white dwarfs in the newly identified LAMOST sample.Comment: 10 pages, 7 figures. Accepted for publication in MNRAS. The full catalogue presented in table 4 is available at http://www2.warwick.ac.uk/fac/sci/physics/research/astro/catalogues/SDSS_WD_candidates_with_LAMOST_spectra.cs

Two-Speed DCT Electric Powertrain Shifting Control and Rig Testing

Dual clutch transmissions (DCTs) are recognized as being suitable for electric drive applications as they can drive with high efficiency and achieve good shifting comfort. A two-speed DCT electric drivetrain is described in this paper, comprised of only two gear pairs and a final drive gear in the two-speed gearbox. The fundamental shifting control algorithm is provided. On the testing rig of University of Technology, Sydney (UTS) powertrain lab, shifting controls and some driving cycle controls were realized. The results demonstrated that the control algorithm functioned well both in transient shifting control process and in the driving cycle conditions

Tunable singlet-triplet splitting in a few-electron Si/SiGe quantum dot

We measure the excited-state spectrum of a Si/SiGe quantum dot as a function of in-plane magnetic field, and we identify the spin of the lowest three eigenstates in an effective two-electron regime. The singlet-triplet splitting is an essential parameter describing spin qubits, and we extract this splitting from the data. We find it to be tunable by lateral displacement of the dot, which is realized by changing two gate voltages on opposite sides of the device. We present calculations showing the data are consistent with a spectrum in which the first excited state of the dot is a valley-orbit state.Comment: 4 pages with 3 figure

Tunable spin-selective loading of a silicon spin qubit

The remarkable properties of silicon have made it the central material for the fabrication of current microelectronic devices. Silicon's fundamental properties also make it an attractive option for the development of devices for spintronics and quantum information processing. The ability to manipulate and measure spins of single electrons is crucial for these applications. Here we report the manipulation and measurement of a single spin in a quantum dot fabricated in a silicon/silicon-germanium heterostructure. We demonstrate that the rate of loading of electrons into the device can be tuned over an order of magnitude using a gate voltage, that the spin state of the loaded electron depends systematically on the loading voltage level, and that this tunability arises because electron spins can be loaded through excited orbital states of the quantum dot. The longitudinal spin relaxation time T1 is measured using single-shot pulsed techniques and found to be ~3 seconds at a field of 1.85 Tesla. The demonstration of single spin measurement as well as a long spin relaxation time and tunability of the loading are all favorable properties for spintronics and quantum information processing applications.Comment: 4 pages, 3 figures, Supplemental Informatio

Exploring Large-scale Structure with Billions of Galaxies

We consider cosmological applications of galaxy number density correlations to be inferred from future deep and wide multi-band optical surveys. We mostly focus on very large scales as a probe of possible features in the primordial power spectrum. We find the proposed survey of the Large Synoptic Survey Telescope may be competitive with future all-sky CMB experiments over a broad range of scales. On very large scales the inferred power spectrum is robust to photometric redshift errors, and, given a sufficient number density of galaxies, to angular variations in dust extinction and photometric calibration errors. We also consider other applications, such as constraining dark energy with the two CMB-calibrated standard rulers in the matter power spectrum, and controlling the effect of photometric redshift errors to facilitate the interpretation of cosmic shear data. We find that deep photometric surveys over wide area can provide constraints that are competitive with spectroscopic surveys in small volumes.Comment: 11 pages, 7 figures, ApJ accepted, references added, expanded discussion in Sec. 3.