1,721,064 research outputs found
Interpolating between boolean and extremely high noisy patterns through minimal dense associative memories
Full text linkRecently, Hopfield and Krotov introduced the concept of dense associative memories [DAM] (close to spin-glasses with P-wise interactions in a disordered statistical mechanical jargon): they proved a number of remarkable features these networks share and suggested their use to (partially) explain the success of the new generation of Artificial intelligence. Thanks to a remarkable ante-litteram analysis by Baldi & Venkatesh, among these properties, it is known these networks can handle a maximal amount of stored patterns K scaling as K ∼ Np-1. In this paper, once introduced a minimal dense associative network as one of the most elementary cost-functions falling in this class of DAM, we sacrifice this high-load regime -namely we force the storage of solely a linear amount of patterns, i.e. K = αN (with α ≥ 0)- to prove that, in this regime, these networks can correctly perform pattern recognition even if pattern signal is O(1) and is embedded in a sea of noise O(√N), also in the large N limit. To prove this statement, by extremizing the quenched free-energy of the model over its natural order-parameters (the various magnetizations and overlaps), we derived its phase diagram, at the replica symmetric level of description and in the thermodynamic limit: as a sideline, we stress that, to achieve this task, aiming at cross-fertilization among disciplines, we pave two hegemon routes in the statistical mechanics of spin glasses, namely the replica trick and the interpolation technique. Both the approaches reach the same conclusion: there is a not-empty region, in the noise-T versus load-α phase diagram plane, where these networks can actually work in this challenging regime; in particular we obtained a quite high critical (linear) load in the (fast) noiseless case resulting in limβ → ∞ αc(β) = 0.65
Tribological behaviour of Ti or Ti alloy vs. zirconia in presence of artificial saliva
Full text linkAbutment is the transmucosal component in a dental implant system and its eventual appearance has a major impact on aesthetics: use of zirconia abutments can be greatly advantageous in avoiding this problem. Both in the case of one and two-piece zirconia abutments, a critical issue is severe wear between the zirconia and titanium components. High friction at this interface can induce loosening of the abutment connection, production of titanium wear debris, and finally, peri-implant gingivitis, gingival discoloration, or marginal bone adsorption can occur. As in vivo wear measurements are highly complex and time-consuming, wear analysis is usually performed in simulators in the presence of artificial saliva. Different commercial products and recipes for artificial saliva are available and the effects of the different mixtures on the tribological behaviour is not widely explored. The specific purpose of this research was to compare two types of artificial saliva as a lubricant in titanium-zirconia contact by using the ball on disc test as a standard tribological test for materials characterisation. Moreover, a new methodology is suggested by using electrokinetic zeta potential titration and contact angle measurements to investigate the chemical stability at the titanium-lubricant interface. This investigation is of relevance both in the case of using zirconia abutments and artificial saliva against chronic dry mouth. Results suggest that an artificial saliva containing organic corrosion inhibitors is able to be firmly mechanically and chemically adsorb on the surface of the Ti c.p. or Ti6Al4V alloy and form a protective film with high wettability. This type of artificial saliva can significantly reduce the friction coefficient and wear of both the titanium and zirconia surfaces. The use of this type of artificial saliva in standard wear tests has to be carefully considered because the wear resistance of the materials can be overestimated while it can be useful in some specific clinical applications. When saliva is free from organic corrosion inhibitors, wear occurs with a galling mechanism. The occurrence of a super-hydrophilic saliva film that is not firmly adsorbed on the surface is not efficient in order to reduce wear. The results give both suggestions about the experimental conditions for lab testing and in vivo performance of components of dental implants when artificial saliva is used
Burnout, reasons for living and dehumanisation among Italian penitentiary police officers
Full text linkThe literature on burnout syndrome among Penitentiary Police Officers (PPOs) is still
rather scarce, and there are no analyses on the protective factors that can prevent these workers
from the dangerous effect of burnout, with respect to the weakening of the reasons for living and
de-humanization. This study aimed to examine the relationships between burnout, protective factors
against weakening of the reasons for living and not desiring to die and the role of de-humanisation,
utilising the Maslach Burnout Inventory (MBI); the Reasons for Living Inventory (RFL); the Testoni
Death Representation Scale (TDRS); and the Human Traits Attribution Scale (HTAS), involving 86
PPOs in a North Italy prison. Results showed the presence of a high level of burnout in the group of
participants. In addition, dehumanization of prisoners, which is considered a factor that could help
in managing other health professional stress situations, does not reduce the level of burnout
Buprenorphine added to levobupivacaine enhances postoperative analgesia of middle interscalene brachial plexus block.
No full textOn the Marchenko-Pastur law in analog bipartite spin-glasses
No full textIn recent decades, statistical mechanics of disordered systems (mainly spin-glasses) has become one of the main tools to investigate complex systems, probably due to the celebrated Replica Symmetry Breaking scheme of Parisi Theory and its deep implications. In this work we consider the analog bipartite spin-glass (or real-valued restricted Boltzmann machine in a neural-network jargon), whose variables (those quenched as well as those dynamical) share standard Gaussian distributions. First, via Guerra's interpolation technique, we express its quenched free-energy in terms of the natural order parameters of the theory (namely the self- and two-replica overlaps), then, we re-obtain the same result by using the replica-trick: a mandatory tribute, given the special occasion. Next, we show that the quenched free-energy of this model is the functional generator of the moments of the matrix whose entries are the correlation coefficients between the weights connecting the two layers of the spin-glass (i.e. the Wishart matrix in random matrix theory or the Hebbian coupling in neural networks): as weights are quenched stochastic variables, this acts as a powerful tool to inspect random matrices. In particular, we find that the Stieltjes transform of the spectral density of the correlation matrix is determined by the (replica-symmetric) quenched free-energy of the bipartite spin-glass model. In this setup, we re-obtain the Marchenko-Pastur law in a very simple way
Dreaming neural networks: Rigorous results
Full text linkRecently, a daily routine for associative neural networks has been proposed: the network Hebbian-learns during the awake state (thus behaving as a standard Hopfield model), then, during its sleep state, it consolidates pure patterns and removes spurious ones, optimizing information storage: this forces the synaptic matrix to collapse to the projector one (ultimately approaching the Kanter-Sompolinsky model), allowing for the maximal critical capacity (for symmetric interactions). So far, this emerging picture (as well as the bulk of papers on unlearning techniques) was supported solely by non-rigorous routes, e.g. replica-trick analysis and numerical simulations, while here we rely on Guerra's interpolation techniques and we extend the generalized stochastic stability approach to the case. Focusing on the replica-symmetric scenario (where the previous investigations lie), the former picture is entirely confirmed. Further, we develop a fluctuation analysis to check where ergodicity is broken (an analysis absent in previous investigations). Remarkably, we find that, as long as the network is awake, ergodicity is bounded by the Amit-Gutfreund-Sompolinsky critical line (as it should), but, as the network sleeps, spin-glass states are destroyed and both the retrieval and the ergodic region get wider. Thus, after a whole sleeping session, the solely surviving regions are the retrieval and ergodic ones, in such a way that the network achieves a perfect retrieval regime
Replica Symmetry Breaking in Dense Hebbian Neural Networks
No full textUnderstanding the glassy nature of neural networks is pivotal both for theoretical and computational advances in Machine Learning and Theoretical Artificial Intelligence. Keeping the focus on dense associative Hebbian neural networks (i.e. Hopfield networks with polynomial interactions of even degree P> 2), the purpose of this paper is twofold: at first we develop rigorous mathematical approaches to address properly a statistical mechanical picture of the phenomenon of replica symmetry breaking (RSB) in these networks, then—deepening results stemmed via these routes—we aim to inspect the glassiness that they hide. In particular, regarding the methodology, we provide two techniques: the former (closer to mathematical physics in spirit) is an adaptation of the transport PDE to this case, while the latter (more probabilistic in its nature) is an extension of Guerra’s interpolation breakthrough. Beyond coherence among the results, either in replica symmetric and in the one-step replica symmetry breaking level of description, we prove the Gardner’s picture (heuristically achieved through the replica trick) and we identify the maximal storage capacity by a ground-state analysis in the Baldi-Venkatesh high-storage regime. In the second part of the paper we investigate the glassy structure of these networks: at difference with the replica symmetric scenario (RS), RSB actually stabilizes the spin-glass phase. We report huge differences w.r.t. the standard pairwise Hopfield limit: in particular, it is known that it is possible to express the free energy of the Hopfield neural network (and, in a cascade fashion, all its properties) as a linear combination of the free energies of a hard spin glass (i.e. the Sherrington–Kirkpatrick model) and a soft spin glass (the Gaussian or ”spherical” model). While this continues to hold also in the first step of RSB for the Hopfield model, this is no longer true when interactions are more than pairwise (whatever the level of description, RS or RSB). For dense networks solely the free energy of the hard spin glass survives. As the Sherrington–Kirkpatrick spin glass is full-RSB (i.e. Parisi theory holds for that model), while the Gaussian spin-glass is replica symmetric, these different representation theorems prove a huge diversity in the underlying glassiness of associative neural networks
Ultrametric identities in glassy models of natural evolution
Full text linkSpin-glasses constitute a well-grounded framework for evolutionary models. Of particular interest for (some of) these models is the lack of self-averaging of their order parameters (e.g. the Hamming distance between the genomes of two individuals), even in asymptotic limits, much as like what happens to the overlap between the configurations of two replica in mean-field spin-glasses. In the latter, this lack of self-averaging is related to a peculiar behavior of the overlap fluctuations, as described by the Ghirlanda–Guerra identities and by the Aizenman–Contucci polynomials, that cover a pivotal role in describing the ultrametric structure of the spin-glass landscape. As for evolutionary mod- els, such identities may therefore be related to a taxonomic classification of individuals, yet a full investigation on their validity is missing. In this paper, we study ultrametric identities in simple cases where solely random mutations take place, while selective pressure is absent, namely in flat landscape models. In particular, we study three paradigmatic models in this setting: the one parent model (which, by construction, is ultrametric at the level of single individu- als), the homogeneous population model (which is replica symmetric), and the species formation model (where a broken-replica scenario emerges at the level of species). We find analytical and numerical evidence that in the first and in the third model nor the Ghirlanda–Guerra neither the Aizenman–Contucci constraints hold, rather a new class of ultrametric identities is satisfied; in the second model all these constraints hold trivially. Very preliminary results on a real biological human genome derived by The 1000 Genome Project Consortium and on two artificial human genomes (generated by two different types neural networks) seem in better agreement with these new identities rather than the classic ones
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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