Comparison of signalized junction control strategies using individual vehicle position data

This paper is concerned with the development of control strategies for urban signalized junction that can make use of individual vehicle position data from localization probes on board the vehicles. Strategy development involves simulating the behaviour of vehicles as they negotiate junctions controlled by prototype strategies and evaluating performance. Two strategies are discussed in this paper, a simple auctioning agent strategy and an extended auctioning agent strategy where a machine learning approach is used to enable agents to be trained by a human expert to improve performance. The performance of these two strategies are compared with each other and with the MOVA algorithm in simulated tests. The results show that auctioning agents using individual vehicle position data can out perform MOVA, but that this performance can be improved further still by using learning auctioning agents trained by a human expert

Role of KNOX genes in the evolution and development of floral nectar spurs

A key question in biology is how changes in gene function or regulation produce new morphologies during evolution. The nectar spur is an evolutionarily labile structure known to influence speciation in a broad range of angiosperm taxa. Here, the genetic basis of nectar spur development, and the evolution of differences in nectar spur morphology, is investigated in Linaria vulgaris and two closely related species of orchid, the primitively longer-spurred Dactylorhiza fuchsii, and more derived short-spurred D. viridis (Orchidinae, Orchidaceae). Despite considerable morphological and phylogenetic differences, nectar spur ontogeny is fundamentally similar in each of the study species, proceeding from an abaxial bulge formed on the ventral petal relatively late in petal morphogenesis. However, spur development is progenetically curtailed in the short-spurred orchid D. viridis. In each case spur development involves class 1 KNOTTED1-like homeobox (KNOX) proteins. KNOX gene expression is not restricted to the spur-bearing petal, indicating that additional components are required to define nectar spur position, e.g. canonical ABC genes, determinants of floral zygomorphy, and additional (currently unknown) factors. However, constitutive expression of class 1 KNOX proteins in transgenic tobacco produces flowers with ectopic outgrowths on the petals, indicating that KNOX proteins alone are, to some degree, capable of inducing structures similar to nectar spurs in a heterologous host. Interestingly, KNOX gene expression is high in the ovary of all study taxa, suggesting that KNOX proteins may also have been involved in the evolution of this key angiosperm feature. Although principally involved in maintaining indeterminacy in the shoot apical meristem (SAM), members of the KNOX gene family have been co-opted in the evolution and development of compound leaves where they suppress differentiation and extend the morphogenetic potential of the leaf. A similar model is presented here to explain the role of KNOX proteins in nectar spur development. Co-option of KNOX gene expression to the maturing perianth delays cellular differentiation, facilitating the development of the nectar spur but requiring additional, unknown factors, to determine nectar spur fate. As facilitators of nectar spur development, changes in the spatio-temporal patterns of KNOX gene expression may alter the potential for nectar spur development and explain the critical length differences observed between the orchids D. fuchsii and D. viridis (and among other angiosperm taxa). Taken together, the available data indicate that KNOX genes confer a meristematic state upon plant tissues in a variety of morphogenetic contexts, making the gene family a potentially versatile tool to mediate a wide variety of evolutionary transformations.Thanks for the generous financial support of the Dept. Plant Sciences, University of Cambridge and Corpus Christi College, Cambridge

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Eclipsing binaries and fast rotators in the Kepler sample. Characterization via radial velocity analysis from Calar Alto

The Kepler mission has provided high-accurate photometric data in a long time span for more than two hundred thousands stars, looking for planetary transits. Among the detected candidates, the planetary nature of around 15% has been established or validated by different techniques. But additional data is needed to characterize the rest of the candidates and reject other possible configurations. We started a follow-up program to validate, confirm, and characterize some of the planet candidates. In this paper we present the radial velocity analysis (RV) of those presenting large variations, compatible with being eclipsing binaries. We also study those showing large rotational velocities, which prevents us from obtaining the necessary precision to detect planetary-like objects. We present new RV results for 13 Kepler objects of interest (KOIs) obtained with the CAFE spectrograph at the Calar Alto Observatory, and analyze their high-spatial resolution images and the Kepler light curves of some interesting cases. We have found five spectroscopic and eclipsing binaries. Among them, the case of KOI-3853 is of particular interest. This system is a new example of the so-called heartbeat stars, showing dynamic tidal distortions in the Kepler light curve. We have also detected duration and depth variations of the eclipse. We suggest possible scenarios to explain such effect, including the presence of a third substellar body possibly detected in our RV analysis. We also provide upper mass limits to the transiting companions of other six KOIs with large rotational velocities. This property prevents the RV method to obtain the necessary precision to detect planetary-like masses. Finally, we analyze the large RV variations of other two KOIs, incompatible with the presence of planetary-mass objects. These objects are likely to be stellar binaries but a longer timespan is still needed.Comment: Accepted for publication in A&A. 18 pages, 9 figures, 17 tables. This version fixes an error affecting the values of tables A.1-A.13. The text remains unaltere

An open-source, stochastic, six-degrees-of-freedom rocket flight simulator, with a probabilistic trajectory analysis approach

Predicting the flight-path of an unguided rocket can help overcome unnecessary risks. Avoiding residential areas or a car-park can improve the safety of launching a rocket significantly. Furthermore, an accurate landing site prediction facilitates recovery. This paper introduces a six-degrees-of-freedom flight simulator for large unguided model rockets that can fly to altitudes of up to 13 km and then return to earth by parachute. The open-source software package assists the user with the design of rockets, and its simulation core models both the rocket flight and the parachute descent in stochastic wind conditions. Furthermore, the uncertainty in the input variables propagates through the model via a Monte Carlo wrapper, simulating a range of possible flight conditions. The resulting trajectories are captured as a Gaussian process, which assists in the statistical assessment of the flight conditions in the face of uncertainties, such as changes in wind conditions, failure to deploy the parachute, and variations in thrust. This approach also facilitates concise presentation of such uncertainties via visualisation of trajectory ensembles

Kepler-447b: a hot-Jupiter with an extremely grazing transit

We present the radial velocity confirmation of the extrasolar planet Kepler-447b, initially detected as a candidate by the Kepler mission. In this work, we analyze its transit signal and the radial velocity data obtained with the Calar Alto Fiber-fed Echelle spectrograph (CAFE). By simultaneously modeling both datasets, we obtain the orbital and physical properties of the system. According to our results, Kepler-447b is a Jupiter-mass planet ($M_p=1.37^{+0.48}_{-0.46}~M_{\rm Jup}$), with an estimated radius of $R_p=1.65^{+0.59}_{-0.56}~R_{\rm Jup}$ (uncertainties provided in this work are $3\sigma$ unless specified). This translates into a sub-Jupiter density. The planet revolves every $\sim7.8$ days in a slightly eccentric orbit ($e=0.123^{+0.037}_{-0.036}$) around a G8V star with detected activity in the Kepler light curve. Kepler-447b transits its host with a large impact parameter ($b=1.076^{+0.112}_{-0.086}$), being one of the few planetary grazing transits confirmed so far and the first in the Kepler large crop of exoplanets. We estimate that only around 20% of the projected planet disk occults the stellar disk. The relatively large uncertainties in the planet radius are due to the large impact parameter and short duration of the transit. Planets with such an extremely large impact parameter can be used to detect and analyze interesting configurations such as additional perturbing bodies, stellar pulsations, rotation of a non-spherical planet, or polar spot-crossing events. All these scenarios would periodically modify the transit properties (depth, duration, and time of mid-transit), what could be detectable with sufficient accurate photometry. Short-cadence photometric data (at the 1 minute level) would help in the search for these exotic configurations in grazing planetary transits like that of Kepler-447b.Comment: Accepted for publication in A&A. 13 pages, 8 figures, 4 tables. This version replaces an earlier version of the pape

Retrodiction with two-level atoms: atomic previvals

In the Jaynes-Cummings model a two-level atom interacts with a single-mode electromagnetic field. Quantum mechanics predicts collapses and revivals in the probability that a measurement will show the atom to be excited at various times after the initial preparation of the atom and field. In retrodictive quantum mechanics we seek the probability that the atom was prepared in a particular state given the initial state of the field and the outcome of a later measurement on the atom. Although this is not simply the time reverse of the usual predictive problem, we demonstrate in this paper that retrodictive collapses and revivals also exist. We highlight the differences between predictive and retrodictive evolutions and describe an interesting situation where the prepared state is essentially unretrodictable.Comment: 15 pages, 3 (5) figure

Bayesian Computing with INLA: A Review

The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically. By developing a nested version of this classical idea, combined with modern numerical techniques for sparse matrices, we obtain the approach of integrated nested Laplace approximations (INLA) to do approximate Bayesian inference for latent Gaussian models (LGMs). LGMs represent an important model abstraction for Bayesian inference and include a large proportion of the statistical models used today. In this review, we discuss the reasons for the success of the INLA approach, the R-INLA package, why it is so accurate, why the approximations are very quick to compute, and why LGMs make such a useful concept for Bayesian computing