33,237 research outputs found
Storage and retrieval of vector beams of light in a multiple-degree-of-freedom quantum memory
The full structuration of light in the transverse plane, including intensity,
phase and polarization, holds the promise of unprecedented capabilities for
applications in classical optics as well as in quantum optics and information
sciences. Harnessing special topologies can lead to enhanced focusing, data
multiplexing or advanced sensing and metrology. Here we experimentally
demonstrate the storage of such spatio-polarization-patterned beams into an
optical memory. A set of vectorial vortex modes is generated via liquid crystal
cell with topological charge in the optic axis distribution, and preservation
of the phase and polarization singularities is demonstrated after retrieval, at
the single-photon level. The realized multiple-degree-of-freedom memory can
find applications in classical data processing but also in quantum network
scenarios where structured states have been shown to provide promising
attributes, such as rotational invariance
Non-Convex Multi-species Hopfield models
In this work we introduce a multi-species generalization of the Hopfield
model for associative memory, where neurons are divided into groups and both
inter-groups and intra-groups pair-wise interactions are considered, with
different intensities. Thus, this system contains two of the main ingredients
of modern Deep neural network architectures: Hebbian interactions to store
patterns of information and multiple layers coding different levels of
correlations. The model is completely solvable in the low-load regime with a
suitable generalization of the Hamilton-Jacobi technique, despite the
Hamiltonian can be a non-definite quadratic form of the magnetizations. The
family of multi-species Hopfield model includes, as special cases, the 3-layers
Restricted Boltzmann Machine (RBM) with Gaussian hidden layer and the
Bidirectional Associative Memory (BAM) model.Comment: This is a pre-print of an article published in J. Stat. Phy
A statistical mechanics approach to autopoietic immune networks
The aim of this work is to try to bridge over theoretical immunology and
disordered statistical mechanics. Our long term hope is to contribute to the
development of a quantitative theoretical immunology from which practical
applications may stem. In order to make theoretical immunology appealing to the
statistical physicist audience we are going to work out a research article
which, from one side, may hopefully act as a benchmark for future improvements
and developments, from the other side, it is written in a very pedagogical way
both from a theoretical physics viewpoint as well as from the theoretical
immunology one.
Furthermore, we have chosen to test our model describing a wide range of
features of the adaptive immune response in only a paper: this has been
necessary in order to emphasize the benefit available when using disordered
statistical mechanics as a tool for the investigation. However, as a
consequence, each section is not at all exhaustive and would deserve deep
investigation: for the sake of completeness, we restricted details in the
analysis of each feature with the aim of introducing a self-consistent model.Comment: 22 pages, 14 figur
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Anergy in self-directed B lymphocytes from a statistical mechanics perspective
The ability of the adaptive immune system to discriminate between self and
non-self mainly stems from the ontogenic clonal-deletion of lymphocytes
expressing strong binding affinity with self-peptides. However, some
self-directed lymphocytes may evade selection and still be harmless due to a
mechanism called clonal anergy. As for B lymphocytes, two major explanations
for anergy developed over three decades: according to "Varela theory", it stems
from a proper orchestration of the whole B-repertoire, in such a way that
self-reactive clones, due to intensive interactions and feed-back from other
clones, display more inertia to mount a response. On the other hand, according
to the `two-signal model", which has prevailed nowadays, self-reacting cells
are not stimulated by helper lymphocytes and the absence of such signaling
yields anergy. The first result we present, achieved through disordered
statistical mechanics, shows that helper cells do not prompt the activation and
proliferation of a certain sub-group of B cells, which turn out to be just
those broadly interacting, hence it merges the two approaches as a whole (in
particular, Varela theory is then contained into the two-signal model). As a
second result, we outline a minimal topological architecture for the B-world,
where highly connected clones are self-directed as a natural consequence of an
ontogenetic learning; this provides a mathematical framework to Varela
perspective. As a consequence of these two achievements, clonal deletion and
clonal anergy can be seen as two inter-playing aspects of the same phenomenon
too
The Use of Rhyme, Rhythm, and Melody as a Form of Repetition Priming to Aid in Encoding, Storage, and Retrieval of Semantic Memories in Alzheimer’s Patients
Millions are diagnosed with Alzheimer’s disease annually which can have debilitating effects on patient memory. Thus, finding new ways to help facilitate memory in these patients, especially through non-pharmaceutical means, has become increasingly important. I examined the use of melody, rhyme, and rhythm as encoding mechanisms to aid in the retrieval of long term semantic information by juxtaposing scholarly articles detailing experiments, each of which examined the effects of various facets of memory facilitation; this helped produce an idea of which devices are most effective. Additionally, I surveyed studies highlighting limitations of song implementation to craft an effective plan to aid Alzheimer’s patients. Melody, rhyme, and rhythm provide an organizational structure to facilitate the encoding of information. Specifically, chunking, the grouping of smaller units into larger ‘chunks’, helps facilitate long term encoding in patients, and is the byproduct of the organizational structure of a text. A major drawback of using these devices is the loss in the depth of encoding semantic information; however, it is important to recognize music still assists general content memory. Therefore, Alzheimer’s patients would benefit from the use of melody as it would provide a moral support, helping familiarity with their surroundings, although they would not benefit from instructional song. Future experiments may study the combination of discussed factors in various settings to examine the unique benefits of music on memory in Alzheimer’s patients
Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit
Restricted Boltzmann machines (RBMs) constitute one of the main models for
machine statistical inference and they are widely employed in Artificial
Intelligence as powerful tools for (deep) learning. However, in contrast with
countless remarkable practical successes, their mathematical formalization has
been largely elusive: from a statistical-mechanics perspective these systems
display the same (random) Gibbs measure of bi-partite spin-glasses, whose
rigorous treatment is notoriously difficult. In this work, beyond providing a
brief review on RBMs from both the learning and the retrieval perspectives, we
aim to contribute to their analytical investigation, by considering two
distinct realizations of their weights (i.e., Boolean and Gaussian) and
studying the properties of their related free energies. More precisely,
focusing on a RBM characterized by digital couplings, we first extend the
Pastur-Shcherbina-Tirozzi method (originally developed for the Hopfield model)
to prove the self-averaging property for the free energy, over its quenched
expectation, in the infinite volume limit, then we explicitly calculate its
simplest approximation, namely its annealed bound. Next, focusing on a RBM
characterized by analogical weights, we extend Guerra's interpolating scheme to
obtain a control of the quenched free-energy under the assumption of replica
symmetry: we get self-consistencies for the order parameters (in full agreement
with the existing Literature) as well as the critical line for ergodicity
breaking that turns out to be the same obtained in AGS theory. As we discuss,
this analogy stems from the slow-noise universality. Finally, glancing beyond
replica symmetry, we analyze the fluctuations of the overlaps for an estimate
of the (slow) noise affecting the retrieval of the signal, and by a stability
analysis we recover the Aizenman-Contucci identities typical of glassy systems.Comment: 21 pages, 1 figur
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