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Neural substrates of mnemonic discrimination: A whole-brain fMRI investigation.
IntroductionA fundamental component of episodic memory is the ability to differentiate new and highly similar events from previously encountered events. Numerous functional magnetic resonance imaging (fMRI) studies have identified hippocampal involvement in this type of mnemonic discrimination (MD), but few studies have assessed MD-related activity in regions beyond the hippocampus. Therefore, the current fMRI study examined whole-brain activity in healthy young adults during successful discrimination of the test phase of the Mnemonic Similarity Task.MethodIn the study phase, participants made "indoor"/"outdoor" judgments to a series of objects. In the test phase, they made "old"/"new" judgments to a series of probe objects that were either repetitions from the memory set (targets), similar to objects in the memory set (lures), or novel. We assessed hippocampal and whole-brain activity consistent with MD using a step function to identify where activity to targets differed from activity to lures with varying degrees of similarity to targets (high, low), responding to them as if they were novel.ResultsResults revealed that the hippocampus and occipital cortex exhibited differential activity to repeated stimuli relative to even highly similar stimuli, but only hippocampal activity predicted discrimination performance.ConclusionsThese findings are consistent with the notion that successful MD is supported by the hippocampus, with auxiliary processes supported by cortex (e.g., perceptual discrimination)
A Multi-variate Discrimination Technique Based on Range-Searching
We present a fast and transparent multi-variate event classification
technique, called PDE-RS, which is based on sampling the signal and background
densities in a multi-dimensional phase space using range-searching. The
employed algorithm is presented in detail and its behaviour is studied with
simple toy examples representing basic patterns of problems often encountered
in High Energy Physics data analyses. In addition an example relevant for the
search for instanton-induced processes in deep-inelastic scattering at HERA is
discussed. For all studied examples, the new presented method performs as good
as artificial Neural Networks and has furthermore the advantage to need less
computation time. This allows to carefully select the best combination of
observables which optimally separate the signal and background and for which
the simulations describe the data best. Moreover, the systematic and
statistical uncertainties can be easily evaluated. The method is therefore a
powerful tool to find a small number of signal events in the large data samples
expected at future particle colliders.Comment: Submitted to NIM, 18 pages, 8 figure
Application of The Method of Elastic Maps In Analysis of Genetic Texts
Abstract - Method of elastic maps ( http://cogprints.ecs.soton.ac.uk/archive/00003088/ and
http://cogprints.ecs.soton.ac.uk/archive/00003919/ )
allows us to construct efficiently 1D, 2D and 3D non-linear approximations to the principal manifolds with different topology (piece of plane, sphere, torus etc.) and to project data onto it. We describe the idea of the method and demonstrate its applications in analysis of genetic sequences. The animated 3D-scatters are available on our web-site: http://www.ihes.fr/~zinovyev/7clusters/
We found the universal cluster structure of genetic sequences, and demonstrated the thin structure of these clusters for coding regions. This thin structure is related to different translational efficiency
Space-by-time non-negative matrix factorization for single-trial decoding of M/EEG activity
We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illustrate the effectiveness of the proposed approach on an EEG dataset recorded during the performance of a visual categorization task. Our method extracts three temporal and two spatial functional components achieving a compact yet full representation of the underlying structure, which validates and summarizes succinctly results from previous studies. Furthermore, we introduce a decoding analysis that allows determining the distinct functional role of each component and relating them to experimental conditions and task parameters. In particular, we demonstrate that the presented stimulus and the task difficulty of each trial can be reliably decoded using specific combinations of components from the identified space-by-time representation. When comparing with a sliding-window linear discriminant algorithm, we show that our approach yields more robust decoding performance across participants. Overall, our findings suggest that the proposed space-by-time decomposition is a meaningful low-dimensional representation that carries the relevant information of single-trial M/EEG signals
A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes
A new background rejection strategy for gamma-ray astrophysics with
stereoscopic Imaging Atmospheric Cherenkov Telescopes (IACT), based on Monte
Carlo (MC) simulations and real background data from the H.E.S.S. [High Energy
Stereoscopic System, see [1].] experiment, is described. The analysis is based
on a multivariate combination of both previously-known and newly-derived
discriminant variables using the physical shower properties, as well as its
multiple images, for a total of eight variables. Two of these new variables are
defined thanks to a new energy evaluation procedure, which is also presented
here. The method allows an enhanced sensitivity with the current generation of
ground-based Cherenkov telescopes to be achieved, and at the same time its main
features of rapidity and flexibility allow an easy generalization to any type
of IACT. The robustness against Night Sky Background (NSB) variations of this
approach is tested with MC simulated events. The overall consistency of the
analysis chain has been checked by comparison of the real gamma-ray signal
obtained from H.E.S.S. observations with MC simulations and through
reconstruction of known source spectra. Finally, the performance has been
evaluated by application to faint H.E.S.S. sources. The gain in sensitivity as
compared to the best standard Hillas analysis ranges approximately from 1.2 to
1.8 depending on the source characteristics, which corresponds to an economy in
observation time of a factor 1.4 to 3.2.Comment: 26 pages, 13 figure
Discrimination and synthesis of recursive quantum states in high-dimensional Hilbert spaces
We propose an interferometric method for statistically discriminating between
nonorthogonal states in high dimensional Hilbert spaces for use in quantum
information processing. The method is illustrated for the case of photon
orbital angular momentum (OAM) states. These states belong to pairs of bases
that are mutually unbiased on a sequence of two-dimensional subspaces of the
full Hilbert space, but the vectors within the same basis are not necessarily
orthogonal to each other. Over multiple trials, this method allows
distinguishing OAM eigenstates from superpositions of multiple such
eigenstates. Variations of the same method are then shown to be capable of
preparing and detecting arbitrary linear combinations of states in Hilbert
space. One further variation allows the construction of chains of states
obeying recurrence relations on the Hilbert space itself, opening a new range
of possibilities for more abstract information-coding algorithms to be carried
out experimentally in a simple manner. Among other applications, we show that
this approach provides a simplified means of switching between pairs of
high-dimensional mutually unbiased OAM bases
Seismic Ray Impedance Inversion
This thesis investigates a prestack seismic inversion scheme implemented in the ray
parameter domain. Conventionally, most prestack seismic inversion methods are
performed in the incidence angle domain. However, inversion using the concept of
ray impedance, as it honours ray path variation following the elastic parameter
variation according to Snellâs law, shows the capacity to discriminate different
lithologies if compared to conventional elastic impedance inversion.
The procedure starts with data transformation into the ray-parameter domain and then
implements the ray impedance inversion along constant ray-parameter profiles. With
different constant-ray-parameter profiles, mixed-phase wavelets are initially estimated
based on the high-order statistics of the data and further refined after a proper well-to-seismic
tie. With the estimated wavelets ready, a Cauchy inversion method is used to
invert for seismic reflectivity sequences, aiming at recovering seismic reflectivity
sequences for blocky impedance inversion. The impedance inversion from reflectivity
sequences adopts a standard generalised linear inversion scheme, whose results are
utilised to identify rock properties and facilitate quantitative interpretation. It has also
been demonstrated that we can further invert elastic parameters from ray impedance
values, without eliminating an extra density term or introducing a Gardnerâs relation
to absorb this term.
Ray impedance inversion is extended to P-S converted waves by introducing the
definition of converted-wave ray impedance. This quantity shows some advantages in
connecting prestack converted wave data with well logs, if compared with the shearwave
elastic impedance derived from the Aki and Richards approximation to the
Zoeppritz equations. An analysis of P-P and P-S wave data under the framework of
ray impedance is conducted through a real multicomponent dataset, which can reduce
the uncertainty in lithology identification.Inversion is the key method in generating those examples throughout the entire thesis
as we believe it can render robust solutions to geophysical problems. Apart from the
reflectivity sequence, ray impedance and elastic parameter inversion mentioned above,
inversion methods are also adopted in transforming the prestack data from the offset
domain to the ray-parameter domain, mixed-phase wavelet estimation, as well as the
registration of P-P and P-S waves for the joint analysis.
The ray impedance inversion methods are successfully applied to different types of
datasets. In each individual step to achieving the ray impedance inversion, advantages,
disadvantages as well as limitations of the algorithms adopted are detailed. As a
conclusion, the ray impedance related analyses demonstrated in this thesis are highly
competent compared with the classical elastic impedance methods and the author
would like to recommend it for a wider application
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure
Algebraic and algorithmic frameworks for optimized quantum measurements
Von Neumann projections are the main operations by which information can be
extracted from the quantum to the classical realm. They are however static
processes that do not adapt to the states they measure. Advances in the field
of adaptive measurement have shown that this limitation can be overcome by
"wrapping" the von Neumann projectors in a higher-dimensional circuit which
exploits the interplay between measurement outcomes and measurement settings.
Unfortunately, the design of adaptive measurement has often been ad hoc and
setup-specific. We shall here develop a unified framework for designing
optimized measurements. Our approach is two-fold: The first is algebraic and
formulates the problem of measurement as a simple matrix diagonalization
problem. The second is algorithmic and models the optimal interaction between
measurement outcomes and measurement settings as a cascaded network of
conditional probabilities. Finally, we demonstrate that several figures of
merit, such as Bell factors, can be improved by optimized measurements. This
leads us to the promising observation that measurement detectors which---taken
individually---have a low quantum efficiency can be be arranged into circuits
where, collectively, the limitations of inefficiency are compensated for
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