Deep Reinforcement Learning with Surrogate Agent-Environment Interface

In this paper, we propose surrogate agent-environment interface (SAEI) in reinforcement learning. We also state that learning based on probability surrogate agent-environment interface provides optimal policy of task agent-environment interface. We introduce surrogate probability action and develop the probability surrogate action deterministic policy gradient (PSADPG) algorithm based on SAEI. This algorithm enables continuous control of discrete action. The experiments show PSADPG achieves the performance of DQN in certain tasks with the stochastic optimal policy nature in the initial training stage

Does Amati Relation Depend on Luminosity of GRB's Host Galaxies?

In order to test systematic of the Amati relation, the 24 long-duration GRBs with firmly determined $E_{\gamma,\mathrm{iso}}$ and $E_{\mathrm p}$ are separated into two sub-groups according to B-band luminosity of their host galaxies. The Amati relations in the two subgroups are found to be in agreement with each other within uncertainties. Taking into account of the well established luminosity - metallicity relation of galaxies, no strong evolution of the Amati relation with GRB's environment metallicity is implied in this study.Comment: 7 pages, 3 figures and 1 table, accepted by ChJA

Electrically Induced Photonic and Acoustic Quantum Effect From Liquid Metal Droplets in Aqueous Solution

So far, several macroscopic quantum phenomena have been discovered in the Josephson junction. Through introducing such a structure with a liquid membrane sandwiched between two liquid metal electrodes, we had ever observed a lighting and sound phenomenon which was explained before as discharge plasma. In fact, such an effect also belongs to a quantum process. It is based on this conceiving, we proposed here that an electrically controllable method can thus be established to generate and manipulate as much photonic quantum as desired. We attributed such electrically induced lighting among liquid metal droplets immersed inside aqueous solution as photonic quantum effect. Our experiments clarified that a small electrical voltage would be strong enough to trigger blue-violet light and sound inside the aqueous solution system. Meanwhile, thermal heat is released, and chemical reaction occurs over the solution. From an alternative way which differs from former effort in interpreting such effect as discharge plasma, we treated this process as a quantum one and derived new conceptual equations to theoretically quantify this phenomenon in light of quantum mechanics principle. It can be anticipated that given specific designing, such spontaneously generated tremendous quantum can be manipulated to entangle together which would possibly help mold functional elements for developing future quantum computing or communication system. With superior adaptability than that of the conventional rigid junction, the present electro-photonic quantum generation system made of liquid metal droplets structure could work in solution, room temperature situation and is easy to be adjusted. It suggests a macroscopic way to innovate the classical strategies and technologies in generating quantum as frequently adopted in classical quantum engineering area.Comment: 13 pages, 6 figure

Sparse Coding and Counting for Robust Visual Tracking

In this paper, we propose a novel sparse coding and counting method under Bayesian framwork for visual tracking. In contrast to existing methods, the proposed method employs the combination of L0 and L1 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve realtime processing, we propose a fast and efficient numerical algorithm for solving the proposed model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Besides, we provide a closed solution of combining L0 and L1 regularized representation to obtain better sparsity. Experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle

While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular approach known as **differential privacy** offers provable privacy guarantees, but it is often observed in practice that it could substantially hamper learning accuracy. In this paper we study the learnability (whether a problem can be learned by any algorithm) under Vapnik's general learning setting with differential privacy constraint, and reveal some intricate relationships between privacy, stability and learnability. In particular, we show that a problem is privately learnable **if an only if** there is a private algorithm that asymptotically minimizes the empirical risk (AERM). In contrast, for non-private learning AERM alone is not sufficient for learnability. This result suggests that when searching for private learning algorithms, we can restrict the search to algorithms that are AERM. In light of this, we propose a conceptual procedure that always finds a universally consistent algorithm whenever the problem is learnable under privacy constraint. We also propose a generic and practical algorithm and show that under very general conditions it privately learns a wide class of learning problems. Lastly, we extend some of the results to the more practical $(\epsilon,\delta)$-differential privacy and establish the existence of a phase-transition on the class of problems that are approximately privately learnable with respect to how small $\delta$ needs to be.Comment: to appear, Journal of Machine Learning Research, 201

Influence of the Nucleon Hard Partons Distribution on J/\Psi Suppression in a GMC Framework

In a Glauber Monte Carlo framework, taking account of the transverse spatial distribution of hard partons in the nucleon, we analyse the nuclear modification factor $R_{dAu}$ for $J/\psi$ in d+Au collisions with the EPS09 shadowing parametrization. After the influence of nucleon hard partons distribution is considered, a clearly upward correction is revealed for the dependence of $R_{dAu}$ on $N_{coll}$ in peripheral d+Au collisions, however, an unconspicuous correction is shown for the results versus $p_{T}$. The theoretical results are in good agreement with the experimental data from PHENIX.Comment: 7pages,2figure

Hadron Multiplicities in p+p and p+Pb Collisions

Experiments at the Large Hadron Collider (LHC) have measured multiplicity distributions in p+p and p+Pb collisions at a new domain of collision energy. Based on considering an energy-dependent broadening of the nucleon's density distribution, charged hadron multiplicities are studied with the phenomenological saturation model and the evolution equation dependent saturation model. By assuming the saturation scale have a small dependence on the 3-dimensional root mean square (rms) radius at different energy, the theoretical results are in good agreement with the experimental data from CMS and ALICE collaboration. Then, the predictive results in p+p collisions at $\sqrt{s}=$ 14 TeV of the LHC are also given

Energy Dependent Growth of Nucleon and Inclusive Charged Hadron Distributions

In the Color Glass Condensate formalism, charged hadron p_{T} distributions in p+p collisions are studied by considering an energy-dependent broadening of nucleon's density distribution. Then, in the Glasma flux tube picture, the n-particle multiplicity distributions at different pseudo-rapidity ranges are investigated. Both of the theoretical results show good agreement with the recent experimental data from ALICE and CMS at \sqrt{s}=0.9, 2.36, 7 TeV. The predictive results for p_{T} and multiplicity distributions in p+p and p+Pb collisions at the Large Hadron Collider are also given in this paper.Comment: 11 pages, 4 figure

On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms

We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information and mutual information. The new definition significantly weakens differential privacy, while preserving its minimalistic design features such as composition over small group and multiple queries as well as closeness to post-processing. Moreover, we show that On-Average KL-Privacy is **equivalent** to generalization for a large class of commonly-used tools in statistics and machine learning that samples from Gibbs distributions---a class of distributions that arises naturally from the maximum entropy principle. In addition, a byproduct of our analysis yields a lower bound for generalization error in terms of mutual information which reveals an interesting interplay with known upper bounds that use the same quantity

A Minimax Theory for Adaptive Data Analysis

In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are often randomized in order to reduce overfitting for such adaptively chosen queries. In this paper, we propose a minimax framework for adaptive data analysis. Assuming Gaussianity of queries, we establish the first sharp minimax lower bound on the squared error in the order of $O(\frac{\sqrt{k}\sigma^2}{n})$, where $k$ is the number of queries asked, and $\sigma^2/n$ is the ordinary signal-to-noise ratio for a single query. Our lower bound is based on the construction of an approximately least favorable adversary who picks a sequence of queries that are most likely to be affected by overfitting. This approximately least favorable adversary uses only one level of adaptivity, suggesting that the minimax risk for 1-step adaptivity with k-1 initial releases and that for $k$-step adaptivity are on the same order. The key technical component of the lower bound proof is a reduction to finding the convoluting distribution that optimally obfuscates the sign of a Gaussian signal. Our lower bound construction also reveals a transparent and elementary proof of the matching upper bound as an alternative approach to Russo and Zou (2015), who used information-theoretic tools to provide the same upper bound. We believe that the proposed framework opens up opportunities to obtain theoretical insights for many other settings of adaptive data analysis, which would extend the idea to more practical realms