127 research outputs found
Renormalized entropy for one dimensional discrete maps: periodic and quasi-periodic route to chaos and their robustness
We apply renormalized entropy as a complexity measure to the logistic and
sine-circle maps. In the case of logistic map, renormalized entropy decreases
(increases) until the accumulation point (after the accumulation point up to
the most chaotic state) as a sign of increasing (decreasing) degree of order in
all the investigated periodic windows, namely, period-2, 3, and 5, thereby
proving the robustness of this complexity measure. This observed change in the
renormalized entropy is adequate, since the bifurcations are exhibited before
the accumulation point, after which the band-merging, in opposition to the
bifurcations, is exhibited. In addition to the precise detection of the
accumulation points in all these windows, it is shown that the renormalized
entropy can detect the self-similar windows in the chaotic regime by exhibiting
abrupt changes in its values. Regarding the sine-circle map, we observe that
the renormalized entropy detects also the quasi-periodic regimes by showing
oscillatory behavior particularly in these regimes. Moreover, the oscillatory
regime of the renormalized entropy corresponds to a larger interval of the
nonlinearity parameter of the sine-circle map as the value of the frequency
ratio parameter reaches the critical value, at which the winding ratio attains
the golden mean.Comment: 14 pages, 7 figure
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Low-resolution and signal-dependent noise distribution in positron emission
tomography (PET) images makes denoising process an inevitable step prior to
qualitative and quantitative image analysis tasks. Conventional PET denoising
methods either over-smooth small-sized structures due to resolution limitation
or make incorrect assumptions about the noise characteristics. Therefore,
clinically important quantitative information may be corrupted. To address
these challenges, we introduced a novel approach to remove signal-dependent
noise in the PET images where the noise distribution was considered as
Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation
(GAT) was used to stabilize varying nature of the PET noise. Other than noise
stabilization, it is also desirable for the noise removal filter to preserve
the boundaries of the structures while smoothing the noisy regions. Indeed, it
is important to avoid significant loss of quantitative information such as
standard uptake value (SUV)-based metrics as well as metabolic lesion volume.
To satisfy all these properties, we extended bilateral filtering method into
trilateral filtering through multiscaling and optimal Gaussianization process.
The proposed method was tested on more than 50 PET-CT images from various
patients having different cancers and achieved the superior performance
compared to the widely used denoising techniques in the literature.Comment: 8 pages, 3 figures; to appear in the Lecture Notes in Computer
Science (MICCAI 2014
Connectivity-Driven Coherence in Complex Networks
We study the emergence of coherence in complex networks of mutually coupled
non-identical elements. We uncover the precise dependence of the dynamical
coherence on the network connectivity, on the isolated dynamics of the elements
and the coupling function. These findings predict that in random graphs, the
enhancement of coherence is proportional to the mean degree. In locally
connected networks, coherence is no longer controlled by the mean degree, but
rather on how the mean degree scales with the network size. In these networks,
even when the coherence is absent, adding a fraction s of random connections
leads to an enhancement of coherence proportional to s. Our results provide a
way to control the emergent properties by the manipulation of the dynamics of
the elements and the network connectivity.Comment: 4 pages, 2 figure
Reply to the Comment by B. Andresen
All the comments made by Andresen's comments are replied and are shown not to
be pertinent. The original discussions [ABE S., Europhys. Lett. 90 (2010)
50004] about the absence of nonextensive statistical mechanics with q-entropies
for classical continuous systems are reinforced.Comment: 5 pages. This is Reply to B. Andresen's Comment on the paper entitled
"Essential discreteness in generalized thermostatistics with non-logarithmic
entropy", Europhys. Lett. 90 (2010) 5000
Learning Optimal Deep Projection of F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
Several diseases of parkinsonian syndromes present similar symptoms at early
stage and no objective widely used diagnostic methods have been approved until
now. Positron emission tomography (PET) with F-FDG was shown to be able
to assess early neuronal dysfunction of synucleinopathies and tauopathies.
Tensor factorization (TF) based approaches have been applied to identify
characteristic metabolic patterns for differential diagnosis. However, these
conventional dimension-reduction strategies assume linear or multi-linear
relationships inside data, and are therefore insufficient to distinguish
nonlinear metabolic differences between various parkinsonian syndromes. In this
paper, we propose a Deep Projection Neural Network (DPNN) to identify
characteristic metabolic pattern for early differential diagnosis of
parkinsonian syndromes. We draw our inspiration from the existing TF methods.
The network consists of a (i) compression part: which uses a deep network to
learn optimal 2D projections of 3D scans, and a (ii) classification part: which
maps the 2D projections to labels. The compression part can be pre-trained
using surplus unlabelled datasets. Also, as the classification part operates on
these 2D projections, it can be trained end-to-end effectively with limited
labelled data, in contrast to 3D approaches. We show that DPNN is more
effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers
Monitoring of prevalent airborne diseases such as COVID-19 characteristically
involves respiratory assessments. While auscultation is a mainstream method for
preliminary screening of disease symptoms, its utility is hampered by the need
for dedicated hospital visits. Remote monitoring based on recordings of
respiratory sounds on portable devices is a promising alternative, which can
assist in early assessment of COVID-19 that primarily affects the lower
respiratory tract. In this study, we introduce a novel deep learning approach
to distinguish patients with COVID-19 from healthy controls given audio
recordings of cough or breathing sounds. The proposed approach leverages a
novel hierarchical spectrogram transformer (HST) on spectrogram representations
of respiratory sounds. HST embodies self-attention mechanisms over local
windows in spectrograms, and window size is progressively grown over model
stages to capture local to global context. HST is compared against
state-of-the-art conventional and deep-learning baselines. Demonstrations on
crowd-sourced multi-national datasets indicate that HST outperforms competing
methods, achieving over 83% area under the receiver operating characteristic
curve (AUC) in detecting COVID-19 cases
Combined In Silico, In Vivo, and In Vitro Studies Shed Insights into the Acute Inflammatory Response in Middle-Aged Mice
We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO2-/NO3- data from "middle-aged" (6-8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for "young" (2-3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging. © 2013 Namas et al
Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict
Horizontal DNA transfer (HDT) is a pervasive mechanism of diversification in many microbial species, but its primary evolutionary role remains controversial. Much recent research has emphasised the adaptive benefit of acquiring novel DNA, but here we argue instead that intragenomic conflict provides a coherent framework for understanding the evolutionary origins of HDT. To test this hypothesis, we developed a mathematical model of a clonally descended bacterial population undergoing HDT through transmission of mobile genetic elements (MGEs) and genetic transformation. Including the known bias of transformation toward the acquisition of shorter alleles into the model suggested it could be an effective means of counteracting the spread of MGEs. Both constitutive and transient competence for transformation were found to provide an effective defence against parasitic MGEs; transient competence could also be effective at permitting the selective spread of MGEs conferring a benefit on their host bacterium. The coordination of transient competence with cell-cell killing, observed in multiple species, was found to result in synergistic blocking of MGE transmission through releasing genomic DNA for homologous recombination while simultaneously reducing horizontal MGE spread by lowering the local cell density. To evaluate the feasibility of the functions suggested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals carrying Streptococcus pneumoniae. This revealed the frequent within-host coexistence of clonally descended cells that differed in their MGE infection status, a necessary condition for the proposed mechanism to operate. Additionally, we found multiple examples of MGEs inhibiting transformation through integrative disruption of genes encoding the competence machinery across many species, providing evidence of an ongoing "arms race." Reduced rates of transformation have also been observed in cells infected by MGEs that reduce the concentration of extracellular DNA through secretion of DNases. Simulations predicted that either mechanism of limiting transformation would benefit individual MGEs, but also that this tactic's effectiveness was limited by competition with other MGEs coinfecting the same cell. A further observed behaviour we hypothesised to reduce elimination by transformation was MGE activation when cells become competent. Our model predicted that this response was effective at counteracting transformation independently of competing MGEs. Therefore, this framework is able to explain both common properties of MGEs, and the seemingly paradoxical bacterial behaviours of transformation and cell-cell killing within clonally related populations, as the consequences of intragenomic conflict between self-replicating chromosomes and parasitic MGEs. The antagonistic nature of the different mechanisms of HDT over short timescales means their contribution to bacterial evolution is likely to be substantially greater than previously appreciated
Atomic-scale authentication using resonant tunnelling diodes
The rapid development of technology has provided a wealth of resources enabling the trust of everyday interactions to be undermined. Authentication schemes aim to address this challenge by providing proof of identity. This can be achieved by using devices that, when challenged, give unique but reproducible responses. At present, these distinct signatures are commonly generated by physically unclonable functions, or PUFs. These devices provide a straightforward measurement of a physical characteristic of their structure that has inherent randomness, due to imperfections in the manufacturing process. These hard-to-predict physical responses can generate a unique identity that can be used for authentication without relying on the secrecy of stored data. However, the classical design of these devices limits both their size and security. Here we show that the extensively studied problematic fluctuations in the current-voltage measurements of resonant tunnelling diodes (RTDs) provide an uncomplicated, robust measurement that can function as a PUF without conventional resource limitations. This is possible due to quantum tunnelling within the RTD, and on account of these room temperature quantum effects, we term such devices QUFs - quantum unclonable functions. As a result of the current-voltage spectra being dependent on the atomic structure and composition of the nanostructure within the RTD, each device provides a high degree of uniqueness, whilst being impossible to clone or simulate, even with state-of-the-art technology. We have thus created PUF-like devices requiring the fewest resources which make use of quantum phenomena in a highly manufacturable electronic device operating at room temperature. Conventional spectral analysis techniques, when applied to our QUFs, will enable reliable generation of unpredictable unique identities which can be employed in advanced authentication systems
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