Recent years have seen increasing interest in the characterization of sub-Neptune sized planets because of their prevalence in the Galaxy, contrasted with their absence in our solar system. HD 97658 is one of the brightest stars hosting a planet of t
his kind, and we present the transmission spectrum of this planet by combining four HST transits, twelve Spitzer/IRAC transits, and eight MOST transits of this system. Our transmission spectrum has higher signal to noise ratio than that from previous works, and the result suggests that the slight increase in transit depth from wavelength 1.1 to 1.7 microns reported in previous works on the transmission spectrum of this planet is likely systematic. Nonetheless, our atmospheric modeling results are not conclusive as no model provides an excellent match to our data. Nonetheless we find that atmospheres with high C/O ratios (C/O >~ 0.8) and metallicities of >~ 100x solar metallicity are favored. We combine the mid-transit times from all the new Spitzer and MOST observations and obtain an updated orbital period of P=9.489295 +/- 0.000005 d, with a best-fit transit time center at T_0 = 2456361.80690 +/- 0.00038 (BJD). No transit timing variations are found in this system. We also present new measurements of the stellar rotation period (34 +/- 2 d) and stellar activity cycle (9.6 yr) of the host star HD 97658. Finally, we calculate and rank the Transmission Spectroscopy Metric of all confirmed planets cooler than 1000 K and with sizes between 1 and 4 R_Earth. We find that at least a third of small planets cooler than 1000 K can be well characterized using JWST, and of those, HD 97658b is ranked fifth, meaning it remains a high-priority target for atmospheric characterization.
Studies of the atmospheres of hot Jupiters reveal a diversity of atmospheric composition and haze properties. Similar studies on individual smaller, temperate planets are rare due to the inherent difficulty of the observations and also to the average
faintness of their host stars. To investigate their ensemble atmospheric properties, we construct a sample of 28 similar planets, all possess equilibrium temperature within 300-500K, have similar size (1-3 R_e), and orbit early M dwarfs and late K dwarfs with effective temperatures within a few hundred Kelvin of one another. In addition, NASAs Kepler/K2 and Spitzer missions gathered transit observations of each planet, producing an uniform transit data set both in wavelength and coarse planetary type. With the transits measured in Keplers broad optical bandpass and Spitzers 4.5 micron wavelength bandpass, we measure the transmission spectral slope, alpha, for the entire sample. While this measurement is too uncertain in nearly all cases to infer the properties of any individual planet, the distribution of alpha among several dozen similar planets encodes a key trend. We find that the distribution of alpha is not well-described by a single Gaussian distribution. Rather, a ratio of the Bayesian evidences between the likeliest 1-component and 2-component Gaussian models favors the latter by a ratio of 100:1. One Gaussian is centered around an average alpha=-1.3, indicating hazy/cloudy atmospheres or bare cores with atmosphere evaporated. A smaller but significant second population (20+-10% of all) is necessary to model significantly higher alpha values, which indicate atmospheres with potentially detectable molecular features. We conclude that the atmospheres of small and temperate planets are far from uniformly flat, and that a subset are particularly favorable for follow-up observation from space-based platforms like HST and JWST.
In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so i
s the cost/regret of pulling a suboptimal arm (e.g., trying a suboptimal network configuration). Therefore, intuitively, we could explore more when the load/price is low and exploit more when the load/price is high. Inspired by this intuition, we propose an Adaptive Upper-Confidence-Bound (AdaUCB) algorithm to adaptively balance the exploration-exploitation tradeoff for opportunistic bandits. We prove that AdaUCB achieves $O(log T)$ regret with a smaller coefficient than the traditional UCB algorithm. Furthermore, AdaUCB achieves $O(1)$ regret with respect to $T$ if the exploration cost is zero when the load level is below a certain threshold. Last, based on both synthetic data and real-world traces, experimental results show that AdaUCB significantly outperforms other bandit algorithms, such as UCB and TS (Thompson Sampling), under large load/price fluctuations.
The occurrence rate of hot Jupiters from the Kepler transit survey is roughly half that of radial velocity surveys targeting solar neighborhood stars. One hypothesis to explain this difference is that the two surveys target stars with different stell
ar metallicity distributions. To test this hypothesis, we measure the metallicity distribution of the Kepler targets using the Hectochelle multi-fiber, high-resolution spectrograph. Limiting our spectroscopic analysis to 610 dwarf stars in our sample with log(g)>3.5, we measure a metallicity distribution characterized by a mean of [M/H]_{mean} = -0.045 +/- 0.00, in agreement with previous studies of the Kepler field target stars. In comparison, the metallicity distribution of the California Planet Search radial velocity sample has a mean of [M/H]_{CPS, mean} = -0.005 +/- 0.006, and the samples come from different parent populations according to a Kolmogorov-Smirnov test. We refit the exponential relation between the fraction of stars hosting a close-in giant planet and the host star metallicity using a sample of dwarf stars from the California Planet Search with updated metallicities. The best-fit relation tells us that the difference in metallicity between the two samples is insufficient to explain the discrepant Hot Jupiter occurrence rates; the metallicity difference would need to be $simeq$0.2-0.3 dex for perfect agreement. We also show that (sub)giant contamination in the Kepler sample cannot reconcile the two occurrence calculations. We conclude that other factors, such as binary contamination and imperfect stellar properties, must also be at play.
With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deploye
d locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay-sensitive applications are executed ahead of delay-tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system.
In cyber-physical systems such as automobiles, measurement data from sensor nodes should be delivered to other consumer nodes such as actuators in a regular fashion. But, in practical systems over unreliable media such as wireless, it is a significan
t challenge to guarantee small enough inter-delivery times for different clients with heterogeneous channel conditions and inter-delivery requirements. In this paper, we design scheduling policies aiming at satisfying the inter-delivery requirements of such clients. We formulate the problem as a risk-sensitive Markov Decision Process (MDP). Although the resulting problem involves an infinite state space, we first prove that there is an equivalent MDP involving only a finite number of states. Then we prove the existence of a stationary optimal policy and establish an algorithm to compute it in a finite number of steps. However, the bane of this and many similar problems is the resulting complexity, and, in an attempt to make fundamental progress, we further propose a new high reliability asymptotic approach. In essence, this approach considers the scenario when the channel failure probabilities for different clients are of the same order, and asymptotically approach zero. We thus proceed to determine the asymptotically optimal policy: in a two-client scenario, we show that the asymptotically optimal policy is a modified least time-to-go policy, which is intuitively appealing and easily implementable; in the general multi-client scenario, we are led to an SN policy, and we develop an algorithm of low computational complexity to obtain it. Simulation results show that the resulting policies perform well even in the pre-asymptotic regime with moderate failure probabilities.
In cyber-physical systems such as in-vehicle wireless sensor networks, a large number of sensor nodes continually generate measurements that should be received by other nodes such as actuators in a regular fashion. Meanwhile, energy-efficiency is als
o important in wireless sensor networks. Motivated by these, we develop scheduling policies which are energy efficient and simultaneously maintain regular deliveries of packets. A tradeoff parameter is introduced to balance these two conflicting objectives. We employ a Markov Decision Process (MDP) model where the state of each client is the time-since-last-delivery of its packet, and reduce it into an equivalent finite-state MDP problem. Although this equivalent problem can be solved by standard dynamic programming techniques, it suffers from a high-computational complexity. Thus we further pose the problem as a restless multi-armed bandit problem and employ the low-complexity Whittle Index policy. It is shown that this problem is indexable and the Whittle indexes are derived. Also, we prove the Whittle Index policy is asymptotically optimal and validate its optimality via extensive simulations.
