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The size of the particle emission region in high-energy collisions can be deduced using the femtoscopic correlations of particle pairs at low relative momentum. Such correlations arise due to quantum statistics and Coulomb and strong final state interactions. In this paper, results are presented from femtoscopic analyses of π±π±, K±K±, K0SK0S, pp, and p¯¯¯p¯¯¯ correlations from Pb-Pb collisions at sNN−−−√=2.76 TeV by the ALICE experiment at the LHC. One-dimensional radii of the system are extracted from correlation functions in terms of the invariant momentum difference of the pair. The comparison of the measured radii with the predictions from a hydrokinetic model is discussed. The pion and kaon source radii display a monotonic decrease with increasing average pair transverse mass mT which is consistent with hydrodynamic model predictions for central collisions. The kaon and proton source sizes can be reasonably described by approximate mT-scaling.
The size of the particle emission region in high-energy collisions can be deduced using the femtoscopic correlations of particle pairs at low relative momentum. Such correlations arise due to quantum statistics and Coulomb and strong final state interactions. In this paper, results are presented from femtoscopic analyses of π±π±, K±K±, K0SK0S, pp, and p¯¯¯p¯¯¯ correlations from Pb-Pb collisions at sNN−−−√=2.76 TeV by the ALICE experiment at the LHC. One-dimensional radii of the system are extracted from correlation functions in terms of the invariant momentum difference of the pair. The comparison of the measured radii with the predictions from a hydrokinetic model is discussed. The pion and kaon source radii display a monotonic decrease with increasing average pair transverse mass mT which is consistent with hydrodynamic model predictions for central collisions. The kaon and proton source sizes can be reasonably described by approximate mT-scaling.
The correlations of identical charged kaons were measured in p-Pb collisions at sNN−−−√=5.02 TeV by the ALICE experiment at the LHC. The femtoscopic invariant radii and correlation strengths were extracted from one-dimensional kaon correlation functions and were compared with those obtained in pp and Pb-Pb collisions at s√=7 TeV and sNN−−−√=2.76 TeV, respectively. The presented results also complement the identical-pion femtoscopic data published by the ALICE collaboration. The extracted radii increase with increasing charged-particle multiplicity and decrease with increasing pair transverse momentum. At comparable multiplicities, the radii measured in p-Pb collisions are found to be close to those observed in pp collisions. The obtained femtoscopic parameters are reproduced by the EPOS 3 hadronic interaction model and disfavor models with large initial size or strong collective expansion at low multiplicities.
The correlations of identical charged kaons were measured in p-Pb collisions at sNN−−−√=5.02 TeV by the ALICE experiment at the LHC. The femtoscopic invariant radii and correlation strengths were extracted from one-dimensional kaon correlation functions and were compared with those obtained in pp and Pb-Pb collisions at s√=7 TeV and sNN−−−√=2.76 TeV, respectively. The presented results also complement the identical-pion femtoscopic data published by the ALICE collaboration. The extracted radii increase with increasing charged-particle multiplicity and decrease with increasing pair transverse momentum. At comparable multiplicities, the radii measured in p-Pb collisions are found to be close to those observed in pp collisions. The obtained femtoscopic parameters are reproduced by the EPOS hadronic interaction model and disfavor models with large initial size or strong collective expansion at low multiplicities.
The correlations of identical charged kaons were measured in p-Pb collisions at sNN−−−√=5.02 TeV by the ALICE experiment at the LHC. The femtoscopic invariant radii and correlation strengths were extracted from one-dimensional kaon correlation functions and were compared with those obtained in pp and Pb-Pb collisions at s√=7 TeV and sNN−−−√=2.76 TeV, respectively. The presented results also complement the identical-pion femtoscopic data published by the ALICE collaboration. The extracted radii increase with increasing charged-particle multiplicity and decrease with increasing pair transverse momentum. At comparable multiplicities, the radii measured in p-Pb collisions are found to be close to those observed in pp collisions. The obtained femtoscopic parameters are reproduced by the EPOS 3 hadronic interaction model and disfavor models with large initial size or strong collective expansion at low multiplicities.
The effect of adding two-way communication to k cells one-way cellular automata (kC-OCAs) on their size of description is studied. kC-OCAs are a parallel model for the regular languages that consists of an array of k identical deterministic finite automata (DFAs), called cells, operating in parallel. Each cell gets information from its right neighbor only. In this paper, two models with different amounts of two-way communication are investigated. Both models always achieve quadratic savings when compared to DFAs. When compared to a one-way cellular model, the result is that minimum two-way communication can achieve at most quadratic savings whereas maximum two-way communication may provide savings bounded by a polynomial of degree k.
At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.
This paper proves correctness of Nocker s method of strictness analysis, implemented for Clean, which is an e ective way for strictness analysis in lazy functional languages based on their operational semantics. We improve upon the work of Clark, Hankin and Hunt, which addresses correctness of the abstract reduction rules. Our method also addresses the cycle detection rules, which are the main strength of Nocker s strictness analysis. We reformulate Nocker s strictness analysis algorithm in a higherorder lambda-calculus with case, constructors, letrec, and a nondeterministic choice operator used as a union operator. Furthermore, the calculus is expressive enough to represent abstract constants like Top or Inf. The operational semantics is a small-step semantics and equality of expressions is defined by a contextual semantics that observes termination of expressions. The correctness of several reductions is proved using a context lemma and complete sets of forking and commuting diagrams. The proof is based mainly on an exact analysis of the lengths of normal order reductions. However, there remains a small gap: Currently, the proof for correctness of strictness analysis requires the conjecture that our behavioral preorder is contained in the contextual preorder. The proof is valid without referring to the conjecture, if no abstract constants are used in the analysis.
The free energy of TAP-solutions for the SK-model of mean field spin glasses can be expressed as a nonlinear functional of local terms: we exploit this feature in order to contrive abstract REM-like models which we then solve by a classical large deviations treatment. This allows to identify the origin of the physically unsettling quadratic (in the inverse of temperature) correction to the Parisi free energy for the SK-model, and formalizes the true cavity dynamics which acts on TAP-space, i.e. on the space of TAP-solutions. From a non-spin glass point of view, this work is the first in a series of refinements which addresses the stability of hierarchical structures in models of evolving populations.