153 research outputs found
Poincar\'e invariance in NRQCD and pNRQCD revisited
We investigate how fields transform under the Poincar\'e group in
nonrelativistic effective field theories of QCD. In constructing these
transformations, we rely only on symmetries and field redefinitions to limit
the number of allowed terms. By requiring invariance of the action under these
transformations, nontrivial relations between Wilson coefficients for both
nonrelativistic QCD and potential nonrelativistic QCD are derived. We show
explicitly how the Poincar\'e algebra is satisfied, and how this gives
complementary information on the Wilson coefficients. We also briefly discuss
the implications of our results, as well as the possibility of applying this
method to other types of effective field theories.Comment: 56 page
Universality classes of interaction structures for NK fitness landscapes
Kauffman's NK-model is a paradigmatic example of a class of stochastic models
of genotypic fitness landscapes that aim to capture generic features of
epistatic interactions in multilocus systems. Genotypes are represented as
sequences of binary loci. The fitness assigned to a genotype is a sum of
contributions, each of which is a random function defined on a subset of loci. These subsets or neighborhoods determine the genetic interactions of
the model. Whereas earlier work on the NK model suggested that most of its
properties are robust with regard to the choice of neighborhoods, recent work
has revealed an important and sometimes counter-intuitive influence of the
interaction structure on the properties of NK fitness landscapes. Here we
review these developments and present new results concerning the number of
local fitness maxima and the statistics of selectively accessible (that is,
fitness-monotonic) mutational pathways. In particular, we develop a unified
framework for computing the exponential growth rate of the expected number of
local fitness maxima as a function of , and identify two different
universality classes of interaction structures that display different
asymptotics of this quantity for large . Moreover, we show that the
probability that the fitness landscape can be traversed along an accessible
path decreases exponentially in for a large class of interaction structures
that we characterize as locally bounded. Finally, we discuss the impact of the
NK interaction structures on the dynamics of evolution using adaptive walk
models.Comment: 61 pages, 9 figure
On the number of limit cycles in asymmetric neural networks
The comprehension of the mechanisms at the basis of the functioning of
complexly interconnected networks represents one of the main goals of
neuroscience. In this work, we investigate how the structure of recurrent
connectivity influences the ability of a network to have storable patterns and
in particular limit cycles, by modeling a recurrent neural network with
McCulloch-Pitts neurons as a content-addressable memory system.
A key role in such models is played by the connectivity matrix, which, for
neural networks, corresponds to a schematic representation of the "connectome":
the set of chemical synapses and electrical junctions among neurons. The shape
of the recurrent connectivity matrix plays a crucial role in the process of
storing memories. This relation has already been exposed by the work of Tanaka
and Edwards, which presents a theoretical approach to evaluate the mean number
of fixed points in a fully connected model at thermodynamic limit.
Interestingly, further studies on the same kind of model but with a finite
number of nodes have shown how the symmetry parameter influences the types of
attractors featured in the system. Our study extends the work of Tanaka and
Edwards by providing a theoretical evaluation of the mean number of attractors
of any given length for different degrees of symmetry in the connectivity
matrices.Comment: 35 pages, 12 figure
Mutation supply and the repeatability of selection for antibiotic resistance
Whether evolution can be predicted is a key question in evolutionary biology.
Here we set out to better understand the repeatability of evolution. We
explored experimentally the effect of mutation supply and the strength of
selective pressure on the repeatability of selection from standing genetic
variation. Different sizes of mutant libraries of an antibiotic resistance
gene, TEM-1 -lactamase in Escherichia coli, were subjected to different
antibiotic concentrations. We determined whether populations went extinct or
survived, and sequenced the TEM gene of the surviving populations. The
distribution of mutations per allele in our mutant libraries- generated by
error-prone PCR- followed a Poisson distribution. Extinction patterns could be
explained by a simple stochastic model that assumed the sampling of beneficial
mutations was key for survival. In most surviving populations, alleles
containing at least one known large-effect beneficial mutation were present.
These genotype data also support a model which only invokes sampling effects to
describe the occurrence of alleles containing large-effect driver mutations.
Hence, evolution is largely predictable given cursory knowledge of mutational
fitness effects, the mutation rate and population size. There were no clear
trends in the repeatability of selected mutants when we considered all
mutations present. However, when only known large-effect mutations were
considered, the outcome of selection is less repeatable for large libraries, in
contrast to expectations. Furthermore, we show experimentally that alleles
carrying multiple mutations selected from large libraries confer higher
resistance levels relative to alleles with only a known large-effect mutation,
suggesting that the scarcity of high-resistance alleles carrying multiple
mutations may contribute to the decrease in repeatability at large library
sizes.Comment: 31pages, 9 figure
On the number of limit cycles in diluted neural networks
We consider the storage properties of temporal patterns, i.e. cycles of
finite lengths, in neural networks represented by (generally asymmetric) spin
glasses defined on random graphs. Inspired by the observation that dynamics on
sparse systems have more basins of attractions than the dynamics of densely
connected ones, we consider the attractors of a greedy dynamics in sparse
topologies, considered as proxy for the stored memories. We enumerate them
using numerical simulation and extend the analysis to large systems sizes using
belief propagation. We find that the logarithm of the number of such cycles is
a non monotonic function of the mean connectivity and we discuss the
similarities with biological neural networks describing the memory capacity of
the hippocampus.Comment: 10 pages, 11 figure
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
Since the recent advent of regulations for data protection (e.g., the General
Data Protection Regulation), there has been increasing demand in deleting
information learned from sensitive data in pre-trained models without
retraining from scratch. The inherent vulnerability of neural networks towards
adversarial attacks and unfairness also calls for a robust method to remove or
correct information in an instance-wise fashion, while retaining the predictive
performance across remaining data. To this end, we consider instance-wise
unlearning, of which the goal is to delete information on a set of instances
from a pre-trained model, by either misclassifying each instance away from its
original prediction or relabeling the instance to a different label. We also
propose two methods that reduce forgetting on the remaining data: 1) utilizing
adversarial examples to overcome forgetting at the representation-level and 2)
leveraging weight importance metrics to pinpoint network parameters guilty of
propagating unwanted information. Both methods only require the pre-trained
model and data instances to forget, allowing painless application to real-life
settings where the entire training set is unavailable. Through extensive
experimentation on various image classification benchmarks, we show that our
approach effectively preserves knowledge of remaining data while unlearning
given instances in both single-task and continual unlearning scenarios.Comment: AAAI 2024 camera ready versio
Inflammation-induced Id2 promotes plasticity in regulatory T cells
T(H)17 cells originating from regulatory T (T-reg) cells upon loss of the T-reg-specific transcription factor Foxp3 accumulate in sites of inflammation and aggravate autoimmune diseases. Whether an active mechanism drives the generation of these pathogenic 'ex-Foxp3 T(H)17' cells, remains unclear. Here we show that pro-inflammatory cytokines enhance the expression of transcription regulator Id2, which mediates cellular plasticity of T-reg into 'ex-Foxp3' T(H)17 cells. Expression of Id2 in in vitro differentiated iT(reg) cells reduces the expression of Foxp3 by sequestration of the transcription activator E2A, leading to the induction of T(H)17-related cytokines. T-reg-specific ectopic expression of Id2 in mice significantly reduces the T-reg compartment and causes immune dysregulation. Cellular fate-mapping experiments reveal enhanced T-reg plasticity compared to wild-type, resulting in exacerbated experimental autoimmune encephalomyelitis pathogenesis or enhanced anti-tumor immunity. Our findings suggest that controlling Id2 expression may provide a novel approach for effective T-reg cell immunotherapies for both autoimmunity and cancer.11sciescopu
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