22,788 research outputs found
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 and 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
-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 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 for 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 on in peripheral d+Au collisions, however,
an unconspicuous correction is shown for the results versus . 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
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
, where is the number of queries asked, and
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 -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
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