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Giant planets like Jupiter and Saturn, play important roles in the formation and habitability of Earth-like planets. The detection of solar system analogs that have multiple cold giant planets is essential for our understanding of planet habitability and planet formation. Although transit surveys such as Kepler and TESS have discovered thousands of exoplanets, these missions are not sensitive to long period planets due to their limited observation baseline. The Tianyu project, comprising two 1-meter telescopes (Tianyu-I and II), is designed to detect transiting cold giant planets in order to find solar system analogs. Featuring a large field of view and equipped with a high-speed CMOS camera, Tianyu-I will perform a high-precision photometric survey of about 100 million stars, measuring light curves at hour-long cadence. The candidates found by Tianyu-I will be confirmed by Tianyu-II and other surveys and follow-up facilities through multi-band photometry, spectroscopy, and high resolution imaging. Tianyu telescopes will be situated at an elevation about 4000 meters in Lenghu, China. With a photometric precision of 1% for stars with V < 18 mag, Tianyu is expected to find more than 300 transiting exoplanets, including about 12 cold giant planets, over five years. A five-year survey of Tianyu would discover 1-2 solar system analogs. Moreover, Tianyu is also designed for non-exoplanetary exploration, incorporating multiple survey modes covering timescales from sub-seconds to months, with a particular emphasis on events occurring within the sub-second to hour range. It excels in observing areas such as infant supernovae, rare variable stars and binaries, tidal disruption events, Be stars, cometary activities, and interstellar objects. These discoveries not only enhance our comprehension of the universe but also offer compelling opportunities for public engagement in scientific exploration., Comment: 48 pages, 16 figures, accepted by Acta Astronomica Sinica

We study a stochastic control/stopping problem with a series of inequality-type and equality-type expectation constraints in a general non-Markovian framework. We demonstrate that the stochastic control/stopping problem with expectation constraints (CSEC) is independent of a specific probability setting and is equivalent to the constrained stochastic control/stopping problem in weak formulation (an optimization over joint laws of Brownian motion, state dynamics, diffusion controls and stopping rules on an enlarged canonical space). Using a martingale-problem formulation of controlled SDEs in spirit of \cite{Stroock_Varadhan}, we characterize the probability classes in weak formulation by countably many actions of canonical processes, and thus obtain the upper semi-analyticity of the CSEC value function. Then we employ a measurable selection argument to establish a dynamic programming principle (DPP) in weak formulation for the CSEC value function, in which the conditional expected costs act as additional states for constraint levels at the intermediate horizon. This article extends the results of \cite{Elk_Tan_2013b} to the expectation-constraint case. We extend our previous work \cite{OSEC_stopping} to the more complicated setting where the diffusion is controlled. Compared to that paper the topological properties of diffusion-control spaces and the corresponding measurability are more technically involved which complicate the arguments especially for the measurable selection for the super-solution side of DPP in the weak formulation., Comment: Keywords: Stochastic control/stopping problem with expectation constraints, martingale-problem formulation, enlarged canonical space, Polish space of diffusion controls, Polish space of stopping times, dynamic programming principle, regular conditional probability distribution, measurable selection

In this paper we compare the numbers of new top 2% researchers from China and USA annually since 1980. We find that the log ratio of the numbers decreases almost linearly over time. As early as 2009, the total number of new top 2% researchers across all subfields from China exceeds that of USA. In particular, such trend is more striking in many subfields, e.g., Engineering, Chemistry, and Enabling & Strategic Technologies.

Accidents that vehicles cross median and enter opposite lane happen frequently, and the existing median barrier has weak anti-collision strength. A new combined median barrier (NCMB) consisted of W-beam guardrail and concrete structure was proposed to decrease deformation and enhance anti-collision strength in this paper. However, there were some uncertainties in the initial design of the NCMB. If the uncertainties were not considered in the design process, the optimization objectives were especially sensitive to the small fluctuation of the variables, and it might result in design failure. For this purpose, the acceleration and deflection were taken as objectives; post thickness, W-beam thickness and post spacing were chosen as design variables; the velocity, mass of vehicle and the yield stress of barrier components were taken as noise factors, a multi-objective robust optimization is carried out for the NCMB based on Taguchi and grey relational analysis (GRA). The results indicate that the acceleration and deflection after optimization are reduced by 47.3% and 76.7% respectively; Signal-to-noise ratio (SNR) of objectives after optimization are increased, it greatly enhances the robustness of the NCMB. The results demonstrate that the effectiveness of the methodology that based on Taguchi method and grey relational analysis.

We analyze an optimal stopping problem with a series of inequality-type and equality-type expectation constraints in a general non-Markovian framework. We show that the optimal stopping problem with expectation constraints (OSEC) in an arbitrary probability setting is equivalent to the constrained problem in weak formulation (optimization over joint laws of stopping rules with Brownian motion and state dynamics on an enlarged canonical space) and thus the OSEC value. Using a martingale-problem formulation, we make an equivalent characterization of the probability classes in weak formulation, which implies that the OSEC value function s upper semi-analytic. Then we exploit a measurable selection argument to establish a dynamic programming principle in weak formulation for the OSEC value function, in which the conditional expected costs act as additional states for constraint levels at the intermediate horizon., Comment: Results are significantly improved. Keywords: Optimal stopping with expectation constraints, % equivalence of different formulations, martingale-problem formulation, enlarged canonical space, Polish space of stopping times, dynamic programming principle, regular conditional probability distribution, measurable selection

We analyze an optimal stopping problem with a constraint on the expected cost. When the reward function and cost function are Lipschitz continuous in state variable, we show that the value of such an optimal stopping problem is a continuous function in current state and in budget level. Then we derive a dynamic programming principle (DPP) for the value function in which the conditional expected cost acts as an additional state process. As the optimal stopping problem with expectation constraint can be transformed to a stochastic optimization problem with supermartingale controls, we explore a second DPP of the value function and thus resolve an open question recently raised in [S. Ankirchner, M. Klein, and T. Kruse, A verification theorem for optimal stopping problems with expectation constraints, Appl. Math. Optim., 2017, pp. 1-33]. Based on these two DPPs, we characterize the value function as a viscosity solution to the related fully non-linear parabolic Hamilton-Jacobi-Bellman equation.

Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models. Such large model is both computation intensive and memory intensive. Deploying such bulky model results in high power consumption and leads to high total cost of ownership (TCO) of a data center. In order to speedup the prediction and make it energy efficient, we first propose a load-balance-aware pruning method that can compress the LSTM model size by 20x (10x from pruning and 2x from quantization) with negligible loss of the prediction accuracy. The pruned model is friendly for parallel processing. Next, we propose scheduler that encodes and partitions the compressed model to each PE for parallelism, and schedule the complicated LSTM data flow. Finally, we design the hardware architecture, named Efficient Speech Recognition Engine (ESE) that works directly on the compressed model. Implemented on Xilinx XCKU060 FPGA running at 200MHz, ESE has a performance of 282 GOPS working directly on the compressed LSTM network, corresponding to 2.52 TOPS on the uncompressed one, and processes a full LSTM for speech recognition with a power dissipation of 41 Watts. Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations. It achieves 40x and 11.5x higher energy efficiency compared with the CPU and GPU respectively., Comment: Accepted as full paper in FPGA'17, Monterey, CA; Also appeared at 1st International Workshop on Efficient Methods for Deep Neural Networks at NIPS 2016, Barcelona, Spain

A novel explanation of the quasielastic release phenomenon in shock compressed aluminum is presented. A dislocation-based model, taking into account dislocation substructures and evolution, is applied to simulate the elastic plastic response of both single crystal and poly crystalline aluminum. The calculated results are in good agreement with experimental results from not only the velocity profiles but also the shear strength and dislocation density, which demonstrates the accuracy of our simulations. Simulated results indicate that dislocation immobilization during dynamic deformation results in a smooth increase of yield stress, which leads to the quasi-elastic release, while the generation of dislocations caused by the plastic release wave results in the appearance of a transition point between the quasielastic release and the plastic release in the profile., Comment: 13 pages and 12 figures

We study a robust Dynkin game over a set of mutually singular probabilities. We first prove that for the conservative player of the game, her lower and upper value processes coincide (i.e. She has a value process $V $ in the game). Such a result helps people connect the robust Dynkin game with second-order doubly reflected backward stochastic differential equations. Also, we show that the value process $V$ is a submartingale under an appropriately defined nonlinear expectations up to the first time $\tau_*$ when $V$ meets the lower payoff process $L$. If the probability set is weakly compact, one can even find an optimal triplet. The mutual singularity of probabilities in causes major technical difficulties. To deal with them, we use some new methods including two approximations with respect to the set of stopping times. The mutual singularity of probabilities causes major technical difficulties. To deal with them, we use some new methods including two approximations with respect to the set of stopping times, Comment: To appear in Annals of Applied Probability. Keywords: robust Dynkin game, nonlinear expectation, dynamic programming principle, controls in weak formulation, weak stability under pasting, martingale approach, path-dependent stochastic differential equations with controls, optimal triplet, optimal stopping with random maturity

We analyze an optimal stopping problem with random maturity under a nonlinear expectation with respect to a weakly compact set of mutually singular probabilities $\mathcal{P}$. The maturity is specified as the hitting time to level $0$ of some continuous index process at which the payoff process is even allowed to have a positive jump. When $\mathcal{P}$ is a collection of semimartingale measures, the optimal stopping problem can be viewed as a {\it discretionary} stopping problem for a player who can influence both drift and volatility of the dynamic of underlying stochastic flow., Comment: Keywords: discretionary stopping, random maturity, controls in weak formulation, optimal stopping, nonlinear expectation, weak stability under pasting, Lipschitz continuous stopping time, dynamic programming principle, martingale approach

We study a doubly reflected backward stochastic differential equation (BSDE) with integrable parameters and the related Dynkin game. When the lower obstacle $L$ and the upper obstacle $U$ of the equation are completely separated, we construct a unique solution of the doubly reflected BSDE by pasting local solutions and show that the $Y-$component of the unique solution represents the value process of the corresponding Dynkin game under $g-$evaluation, a nonlinear expectation induced by BSDEs with the same generator $g$ as the doubly reflected BSDE concerned. In particular, the first time when process $Y $ meets $L$ and the first time when process $Y $ meets $U$ form a saddle point of the Dynkin game., Comment: Final version. To appear in Stochastic Processes and Their Applications

This paper considers the supercritical Navier-Stokes equations posed in the whole space $\R^d$, with suitably randomized initial data, in the weak solution setting. The global weak solutions are constructed for a large set of initial data in $H^{-s}(\R^d)$ for some $s>0$ via a probabilistic argument, and this in turn implies the almost sure existence., Comment: This is a short version of our research note which draws heavily from arXiv:1204.5444

With a sky brightness monitor in Xinglong station of National Astronomical Observatories of China (NAOC), we collected data from 22 dark clear nights and 90 lunar nights. We first measured the sky brightness variation with time in dark nights, found a clear correlation between the sky brightness and human activity. Then with a modified sky brightness model of moon night and data from moon night, we derived the typical value for several important parameters in the model. With these results, we calculated the sky brightness distribution under a given moon condition for Xinglong station. Furthermore, we simulated the moon night sky brightness distribution in a 5 degree field of view telescope (such as LAMOST). These simulations will be helpful to determine the magnitude limit, exposure time as well as the survey design for LAMOST at lunar night., Comment: 16 pages, 9 figures, accepted by RAA

We study a robust optimal stopping problem with respect to a set $\cP$ of mutually singular probabilities. This can be interpreted as a zero-sum controller-stopper game in which the stopper is trying to maximize its pay-off while an adverse player wants to minimize this payoff by choosing an evaluation criteria from $\cP$. We show that the \emph{upper Snell envelope $\ol{Z}$} of the reward process $Y$ is a supermartingale with respect to an appropriately defined nonlinear expectation $\ul{\sE}$, and $\ol{Z}$ is further an $\ul{\sE}-$martingale up to the first time $\t^*$ when $\ol{Z}$ meets $Y$. Consequently, $\t^*$ is the optimal stopping time for the robust optimal stopping problem and the corresponding zero-sum game has a value. Although the result seems similar to the one obtained in the classical optimal stopping theory, the mutual singularity of probabilities and the game aspect of the problem give rise to major technical hurdles, which we circumvent using some new methods., Comment: Final Version, 50 pages. This is a much more comprehensive version of what appeared in the SIAM Journal on Control and Optimization

We analyze a zero-sum stochastic differential game between two competing players who can choose unbounded controls. The payoffs of the game are defined through backward stochastic differential equations. We prove that each player's priority value satisfies a weak dynamic programming principle and thus solves the associated fully non-linear partial differential equation in the viscosity sense., Comment: Key words: Zero-sum stochastic differential games, Elliott-Kalton strategies, weak dynamic programming principle, backward stochastic differential equations, viscosity solutions, fully non-linear PDEs. A shorter version is to appear in the SIAM Journal on Control and Optimization

The weather at Xinglong Observing Station, where the Guo Shou Jing Telescope (GSJT) is located, is strongly affected by the monsoon climate in north-east China. The LAMOST survey strategy is constrained by these weather patterns. In this paper, we present a statistics on observing hours from 2004 to 2007, and the sky brightness, seeing, and sky transparency from 1995 to 2011 at the site. We investigate effects of the site conditions on the survey plan. Operable hours each month shows strong correlation with season: on average there are 8 operable hours per night available in December, but only 1-2 hours in July and August. The seeing and the sky transparency also vary with seasons. Although the seeing is worse in windy winters, and the atmospheric extinction is worse in the spring and summer, the site is adequate for the proposed scientific program of LAMOST survey. With a Monte Carlo simulation using historical data on the site condition, we find that the available observation hours constrain the survey footprint from 22h to 16h in right ascension; the sky brightness allows LAMOST to obtain the limit magnitude of V = 19.5mag with S/N = 10., Comment: 10 pages, 8 figures, accepted for publication in RAA

We generalize the results of Fleming and Souganidis (1989) on zero sum stochastic differential games to the case when the controls are unbounded. We do this by proving a dynamic programming principle using a covering argument instead of relying on a discrete approximation (which is used along with a comparison principle by Fleming and Souganidis). Also, in contrast with Fleming and Souganidis, we define our pay-off through a doubly reflected backward stochastic differential equation. The value function (in the degenerate case of a single controller) is closely related to the second order doubly reflected BSDEs., Comment: Key Words: Zero-sum stochastic differential games, Elliott-Kalton strategies, dynamic programming principle, stability under pasting, doubly reflected backward stochastic differential equations, viscosity solutions, obstacle problem for fully non-linear PDEs, shifted processes, shifted SDEs, second-order doubly reflected backward stochastic differential equations

Given $p \in (1, 2)$, we study $L^p$-solutions of a multi-dimensional backward stochastic differential equation with jumps (BSDEJ) whose generator may not be Lipschitz continuous in $(y,z)-$variables. We show that such a BSDEJ with a p-integrable terminal data admits a unique $L^p$ solution by approximating the monotonic generator by a sequence of Lipschitz generators via convolution with mollifiers and using a stability result., Comment: Keywords: Backward stochastic differential equations with jumps, $L^p$ solutions, monotonic generators, convolution with mollifiers

In this paper, we analyze a real-valued reflected backward stochastic differential equation (RBSDE) with an unbounded obstacle and an unbounded terminal condition when its generator $f$ has quadratic growth in the $z$-variable. In particular, we obtain existence, comparison, and stability results, and consider the optimal stopping for quadratic $g$-evaluations. As an application of our results we analyze the obstacle problem for semi-linear parabolic PDEs in which the non-linearity appears as the square of the gradient. Finally, we prove a comparison theorem for these obstacle problems when the generator is convex or concave in the $z$-variable., Comment: Key Words: Quadratic reflected backward stochastic differential equations, convex/concave generator, $\th$-difference method, Legenre-Fenchel duality, optimal stopping problems for quadratic $g$-evaluations, stability, obstacle problems for semi-linear parabolic PDEs, viscosity solutions

We use martingale and stochastic analysis techniques to study a continuous-time optimal stopping problem, in which the decision maker uses a dynamic convex risk measure to evaluate future rewards. We also find a saddle point for an equivalent zero-sum game of control and stopping, between an agent (the "stopper") who chooses the termination time of the game, and an agent (the "controller", or "nature") who selects the probability measure., Comment: Keywords: Convex risk measures, continuous-time optimal stopping, robustness methods

In this paper we extend the notion of g-evaluation, in particular g-expectation, to the case where the generator g is allowed to have a quadratic growth. We show that some important properties of the g-expectations, including a representation theorem between the generator and the corresponding g-expectation, and consequently the reverse comparison theorem of quadratic BSDEs as well as the Jensen inequality, remain true in the quadratic case. Our main results also include a Doob-Meyer type decomposition, the optional sampling theorem, and the up-crossing inequality. The results of this paper are important in the further development of the general quadratic nonlinear expectations., Comment: 27 pages

We develop a theory for solving continuous time optimal stopping problems for non-linear expectations. Our motivation is to consider problems in which the stopper uses risk measures to evaluate future rewards., Comment: Key Words: Nonlinear Expectations, Optimal Stopping, Snell envelope, Stability, g-expectations

In this paper we extend the notion of ``filtration-consistent nonlinear expectation" (or "${\cal F}$-consistent nonlinear expectation") to the case when it is allowed to be dominated by a $g$-expectation that may have a quadratic growth. We show that for such a nonlinear expectation many fundamental properties of a martingale can still make sense, including the Doob-Meyer type decomposition theorem and the optional sampling theorem. More importantly, we show that any quadratic ${\cal F}$-consistent nonlinear expectation with a certain domination property must be a quadratic $g$-expectation. The main contribution of this paper is the finding of the domination condition to replace the one used in all the previous works, which is no longer valid in the quadratic case. We also show that the representation generator must be deterministic, continuous, and actually must be of the simple form.