81,954 research outputs found
A highly accurate spike sorting processor with reconfigurable embedded frames for unsupervised and adaptive analysis of neural signals
Future implantable devices demand ultra-low power consumption with self-calibration capability providing real-time processing of biomedical signals. This paper introduces an adaptive processing framework for highly accurate on-chip spike sorting processing by learning the signal model in the recorded neural data. The novel adaptive spike sorting processor employs dual thresholding detection, adaptive feature extraction and online clustering with sorting threshold self-tuning capability. A prototype chip was fabricated in 180 nm CMOS technology. It achieves 84.5% overall clustering accuracy, provides up to 240X data reduction and consumes 148 ÎĽW of power from a 1.8 V supply voltage
An HMM-based Comparative Genomic Framework for Detecting Introgression in Eukaryotes
One outcome of interspecific hybridization and subsequent effects of
evolutionary forces is introgression, which is the integration of genetic
material from one species into the genome of an individual in another species.
The evolution of several groups of eukaryotic species has involved
hybridization, and cases of adaptation through introgression have been already
established. In this work, we report on a new comparative genomic framework for
detecting introgression in genomes, called PhyloNet-HMM, which combines
phylogenetic networks, that capture reticulate evolutionary relationships among
genomes, with hidden Markov models (HMMs), that capture dependencies within
genomes. A novel aspect of our work is that it also accounts for incomplete
lineage sorting and dependence across loci.
Application of our model to variation data from chromosome 7 in the mouse
(Mus musculus domesticus) genome detects a recently reported adaptive
introgression event involving the rodent poison resistance gene Vkorc1, in
addition to other newly detected introgression regions. Based on our analysis,
it is estimated that about 12% of all sites withinchromosome 7 are of
introgressive origin (these cover about 18 Mbp of chromosome 7, and over 300
genes). Further, our model detects no introgression in two negative control
data sets. Our work provides a powerful framework for systematic analysis of
introgression while simultaneously accounting for dependence across sites,
point mutations, recombination, and ancestral polymorphism
The case for absolute ligand discrimination : modeling information processing and decision by immune T cells
Some cells have to take decision based on the quality of surroundings
ligands, almost irrespective of their quantity, a problem we name "absolute
discrimination". An example of absolute discrimination is recognition of
not-self by immune T Cells. We show how the problem of absolute discrimination
can be solved by a process called "adaptive sorting". We review several
implementations of adaptive sorting, as well as its generic properties such as
antagonism. We show how kinetic proofreading with negative feedback implements
an approximate version of adaptive sorting in the immune context. Finally, we
revisit the decision problem at the cell population level, showing how
phenotypic variability and feedbacks between population and single cells are
crucial for proper decision
Dopamine and the development of executive dysfunction in autism spectrum disorders.
Persons with autism regularly exhibit executive dysfunction (ED), including problems with deliberate goal-directed behavior, planning, and flexible responding in changing environments. Indeed, this array of deficits is sufficiently prominent to have prompted a theory that executive dysfunction is at the heart of these disorders. A more detailed examination of these behaviors reveals, however, that some aspects of executive function remain developmentaly appropriate. In particular, while people with autism often have difficulty with tasks requiring cognitive flexibility, their fundamental cognitive control capabilities, such as those involved in inhibiting an inappropriate but relatively automatic response, show no significant impairment on many tasks. In this article, an existing computational model of the prefrontal cortex and its role in executive control is shown to explain this dichotomous pattern of behavior by positing abnormalities in the dopamine-based modulation of frontal systems in individuals with autism. This model offers excellent qualitative and quantitative fits to performance on standard tests of cognitive control and cognitive flexibility in this clinical population. By simulating the development of the prefrontal cortex, the computational model also offers a potential explanation for an observed lack of executive dysfunction early in life
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
Enhanced goal-oriented error assessment and computational strategies in adaptive reduced basis solver for stochastic problems
This work focuses on providing accurate low-cost approximations of stochastic Âżnite elements simulations in the framework of linear elasticity. In a previous work, an adaptive strategy was introduced as an improved Monte-Carlo method for multi-dimensional large stochastic problems. We provide here a complete analysis of the method including a new enhanced goal-oriented error estimator and estimates of CPU (computational processing unit) cost gain. Technical insights of these two topics are presented in details, and numerical examples show the interest of these new developments.Postprint (author's final draft
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