5,359 research outputs found
Initial energy density and gluon distribution from the Glasma in heavy-ion collisions
We estimate the energy density and the gluon distribution associated with the
classical fields describing the early-time dynamics of the heavy-ion
collisions. We first decompose the energy density into the momentum components
exactly in the McLerran-Venugopalan model, with the use of the Wilson line
correlators. Then we evolve the energy density with the free-field equation,
which is justified by the dominance of the ultraviolet modes near the collision
point. We also discuss the improvement with inclusion of nonlinear terms into
the time evolution. Our numerical results at RHIC energy are fairly consistent
with the empirical values.Comment: 14 pages, 8 figures, 3 table
Two-color quark matter: U(1)_A restoration, superfluidity, and quarkyonic phase
We discuss the phase structure of quantum chromodynamics (QCD) with two
colors and two flavors of light quarks. This is motivated by the increasing
interest in the QCD phase diagram as follows: (1) The QCD critical point search
has been under intensive dispute and its location and existence suffer from
uncertainty of effective U(1)_A symmetry restoration. (2) A new phase called
quarkyonic matter is drawing theoretical and experimental attention but it is
not clear whether it can coexist with diquark condensation. We point out that
two-color QCD is nontrivial enough to contain essential ingredients for (1) and
(2) both, and most importantly, is a system without the sign problem in
numerical simulations on the lattice. We adopt the two-flavor
Nambu-Jona-Lasinio model extended with the two-color Polyakov loop and make
quantitative predictions which can be tested by lattice simulations.Comment: 14 pages, REVTeX4, 12 eps figures; v2: version published in Phys.
Rev. D; v3: an error in the Appendix fixed, Fig. 9 modified accordingl
Are Muslims the New Catholics? Europe’s Headscarf Laws in Comparative Historical Perspective
In this paper a biologically-inspired model for partly occluded patterns is proposed. The model is based on the hypothesis that in human visual system occluding patterns play a key role in recognition as well as in reconstructing internal representation for a pattern’s occluding parts. The proposed model is realized with a bidirectional hierarchical neural network. In this network top-down cues, generated by direct connections from the lower to higher levels of hierarchy, interact with the bottom-up information, generated from the un-occluded parts, to recognize occluded patterns. Moreover, positional cues of the occluded as well as occluding patterns, that are computed separately but in the same network, modulate the top-down and bottom-up processing to reconstruct the occluded patterns. Simulation results support the presented hypothesis as well as effectiveness of the model in providing a solution to recognition of occluded patterns. The behavior of the model is in accordance to the known human behavior on the occluded patterns
Views of the Chiral Magnetic Effect
My personal views of the Chiral Magnetic Effect are presented, which starts
with a story about how we came up with the electric-current formula and
continues to unsettled subtleties in the formula. There are desirable features
in the formula of the Chiral Magnetic Effect but some considerations would lead
us to even more questions than elucidations. The interpretation of the produced
current is indeed very non-trivial and it involves a lot of confusions that
have not been resolved.Comment: 19 pages, no figure; typos corrected, references significantly
updated, to appear in Lect. Notes Phys. "Strongly interacting matter in
magnetic fields" (Springer), edited by D. Kharzeev, K. Landsteiner, A.
Schmitt, H.-U. Ye
Heavy quark potential in the instanton liquid model
We study the heavy quark potential in the instanton liquid model by carefully
measuring Wilson loops out to a distance of order 3. A random instanton
ensemble with a fixed radius = 1/3 generates a potential
growing very slowly at large . In contrast, a more realistic size
distribution growing as at small and decaying as at
large , leads to a potential which grows linearly with . The string
tension, however, is only about 1/10 of the phenomenological value.Comment: LATTICE98(confine
Second-order and Fluctuation-induced First-order Phase Transitions with Functional Renormalization Group Equations
We investigate phase transitions in scalar field theories using the
functional renormalization group (RG) equation. We analyze a system with
U(2)xU(2) symmetry, in which there is a parameter that controls the
strength of the first-order phase transition driven by fluctuations. In the
limit of \lambda_2\to0\epsilon$-expansion results. We compare results from the expansion and from
the full numerical calculation and find that the fourth-order expansion is only
of qualitative use and that the sixth-order expansion improves the quantitative
agreement.Comment: 15 pages, 10 figures, major revision; discussions on O(N) models
reduced, a summary section added after Introduction, references added; to
appear in PR
Invariant, super and quasi-martingale functions of a Markov process
We identify the linear space spanned by the real-valued excessive functions
of a Markov process with the set of those functions which are quasimartingales
when we compose them with the process. Applications to semi-Dirichlet forms are
given. We provide a unifying result which clarifies the relations between
harmonic, co-harmonic, invariant, co-invariant, martingale and co-martingale
functions, showing that in the conservative case they are all the same.
Finally, using the co-excessive functions, we present a two-step approach to
the existence of invariant probability measures
Are You Tampering With My Data?
We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary.Comment: 18 page
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