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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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371013 · Jun 202019922001200920182026
48 results for virus assembly

Machine learning speeds up the construction of virus assembly fitness landscapes.

problem Constructing realistic evolutionary fitness landscapes for viruses is computationally expensive.
method Developed a neural network to model virus assembly efficiency from a whole genome/phenotype space.
result Machine learning significantly reduces the computational time for constructing fitness landscapes.

Enhanced Zika spread forecasting using topological data analysis.

problem Challenging prediction of Zika virus spread due to nonlinear spatio-temporal dependency and lack of historical records.
method Integrates topological data analysis, specifically persistent homology, into predictive machine learning models.
result Ensemble forecasting improves Zika spread predictions in Brazil.

Study assesses sugar beet yields under EU's neonicotinoids ban and climate change.

problem Impact of yellow virus on sugar beet yields under neonicotinoids ban and climate change.
method Modeling using climate datasets and simulations of aphid flight and abundance.
result Reconstructs sugar beet yields using 'as if' approach without neonicotinoids.

For every strong coarse homology theory we construct a coarse assembly map as a natural transformation between coarse homology theories. We provide various conditions implying that this assembly map is an equivalence. These results generalize known results for the analytic coarse assembly map for K-homology to general …

2017-06-07abs ↗pdf ↗

Model predicts composite structures assembly quality with input uncertainty.

problem Accurate prediction of dimensional deviations and residual stress in composite structures assembly.
method Neural Network Gaussian Process considering input uncertainty.
result NNGPIU model outperforms other methods for nonsmooth, nonlinear responses.

If a Lie algebra structures $\gG$ on a vector space is the sum of a family of mutually compatible Lie algebra structures $\gG_i$, we say that $\gG$ is \emph{simply assembled} from $\gG_s$'s. By repeating this procedure several times one gets a family of Lie algebras \emph{assembled} from $\gG_s$'s. The central result o…

2012-05-28abs ↗pdf ↗

We construct the geometric Baum-Connes assembly map for twisted Lie groupoids, that means for Lie groupoids together with a given groupoid equivariant PU(H)PU(H)-principle bundle. The construction is based on the use of geometric deformation groupoids, these objects allow in particular to give a geometric construction of …

2014-02-14abs ↗pdf ↗

Study assesses linear classifiers for virus genotyping and subtyping.

problem Challenges in classifying viral sequences, especially in alignment-free methods.
method Comprehensive evaluation of linear classifiers on HCV genomes, varying parameters and sequence lengths.
result Several classifiers perform well under specific conditions, providing robust assessment.

We study in this paper the maximal version of the coarse Baum-Connes assembly map for families of expanding graphs arising from residually finite groups. Unlike for the usual Roe algebra, we show that this assembly map is closely related to the (maximal) Baum-Connes assembly map for the group and is an isomorphism for …

2009-02-13abs ↗pdf ↗

Semi-supervised deep learning detects problematic reads for genome assembly.

problem De novo genome assembly is hindered by specific types of reads.
method Analysis of coverage graphs converted to 1D-signals using semi-supervised deep learning models.
result Semi-supervised deep learning models can detect problematic reads with minimal labeled data.

Controlled KK-theory is used to show that algebraic KK-theory of virtually abelian groups is described by an assembly map defined using possibly-infinite hyperelementary subgroups. The Farrell-Jones summand (coming from infinite subgroups) is parameterized by the rational projective space of the group, and a reduced …

2005-09-13abs ↗pdf ↗

If a Lie algebra structure g on a vector space is the sum of a family of mutually compatible Lie algebra structures g_i's, we say that g is simply assembled from the g_i's. Repeating this procedure with a number of Lie algebras, themselves simply assembled from the g_i's, one obtains a Lie algebra assembled in two step…

2017-07-14abs ↗pdf ↗

We use assembly maps to study TC(A[G];p)\mathbf{TC}(\mathbb{A}[G];p), the topological cyclic homology at a prime pp of the group algebra of a discrete group GG with coefficients in a connective ring spectrum A\mathbb{A}. For any finite group, we prove that the assembly map for the family of cyclic subgroups is an isomorphis…

2016-07-13abs ↗pdf ↗

In this article, we introduce the notion of a functor on coarse spaces being coarsely excisive- a coarse analogue of the notion of a functor on topological spaces being excisive. Further, taking cones, a coarsely excisive functor yields a topologically excisive functor, and for coarse topological spaces there is an ass…

2010-02-24abs ↗pdf ↗

Defines and computes geometric pairings for discrete groups using Baum-Connes assembly map.

problem Defining and computing geometric pairings for discrete countable groups.
method Constructs explicit morphisms and the Chern-Baum-Connes assembly map.
result Explicit formulation of a Chern-Connes pairing with the periodic cyclic cohomology of the group algebra.

Optimizes lockdown strategies to balance economic activities and virus spread.

problem Balancing economic activities and virus spread during lockdowns.
method Modeling the pandemic as SEIR, applying Granovetter threshold model for social distancing, and using NSGA-II optimization.
result Optimal lockdown policies for ten weeks to minimize infections and economic impact.

The application of machine learning to bioinformatics problems is well established. Less well understood is the application of bioinformatics techniques to machine learning and, in particular, the representation of non-biological data as biosequences. The aim of this paper is to explore the effects of giving amino acid…

2013-02-15abs ↗pdf ↗

Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.

problem Detecting structure in noisy or approximate repeats of patterns in sparse binary data.
method Probabilistic binary latent variable model based on Noisy-OR model, inferring sparse activity in latent variables.
result Model successfully extracts and explains latent structure in spiking neural data.

We construct a higher Whitehead torsion map, using algebraic K-theory of spaces, and show that it satisfies the usual properties of the classical Whitehead torsion. This is used to describe a "geometric assembly map" defined on stabilized structure spaces in purely homotopy theoretic terms.

2011-05-11abs ↗pdf ↗

BEAN models neuronal correlations to create interpretable representations.

problem Hard interpretation of dense-layer representations in DNNs.
method Inspired by neuroscience, BEAN models neuronal correlations and dependencies.
result BEAN enables formation of interpretable neuronal clusters without sacrificing model performance.

In this paper we introduce a homotopy theoretic technique for proving that the KK-theoretic assembly map is an equivalence. It is an extension of the methods used to prove split injectivity of the assembly and applies to any geometrically finite group. Our result is that there are two requirements which need to hold. …

2013-05-15abs ↗pdf ↗

Bayesian calibration for BCP self-assembly models using image data and measure transport.

problem Calibrating models of BCP self-assembly from image data with aleatory uncertainty.
method Likelihood-free inference via measure transport and summary statistics.
result Expected information gains can be computed efficiently for model calibration.

Between the category of exact metric spaces with bounded geometry (about which much is known) and the larger category of arbitrary exact metric spaces (about which little is known) lies the intermediate category of asymptotically exact metric spaces. We show that the coarse Baum-Connes assembly map is naturally split s…

2012-06-13abs ↗pdf ↗

Financial contagion spreads through international relations, posing systemic risk.

problem Systemic risk in global financial networks due to debt crises.
method Epidemiological model applied to a network of European countries using bilateral foreign claims data.
result Countries experiencing debt crises can threaten global financial stability, akin to a 'financial virus'.

In this paper, the first of a series of two, we continue the study of higher index theory for expanders. We prove that if a sequence of graphs is an expander and the girth of the graphs tends to infinity, then the coarse Baum-Connes assembly map is injective, but not surjective, for the associated metric space XX. Exp…

2010-12-19abs ↗pdf ↗

New proof of index theorem for topological manifold bundles.

problem Index theorem for fiber bundles of compact topological manifolds.
method Use of a convenient framework for bivariant theories and recent results on the homotopy type of the topological cobordism category.
result Refinement of the assembly map for an extended A-theory characteristic.

This paper provides a full controlled version of algebraic KK-theory. This includes a rich array of assembly maps; the controlled assembly isomorphism theorem identifying the controlled group with homology; and the stability theorem describing the behavior of the inverse limit as the control parameter goes to 0. There…

2004-02-24abs ↗pdf ↗

We prove that the Farrell-Jones assembly map for connective algebraic K-theory is rationally injective, under mild homological finiteness conditions on the group and assuming that a weak version of the Leopoldt-Schneider conjecture holds for cyclotomic fields. This generalizes a result of Bökstedt, Hsiang, and Madsen, …

2015-04-14abs ↗pdf ↗

This paper studies how modular agents can learn to control complex morphologies.

problem Contemporary sensorimotor learning starts with existing complex agents, but this paper explores learning from primitive, modular agents.
method A collection of primitive agents learns to dynamically self-assemble into composite bodies and coordinate their behavior to control these bodies.
result Dynamic and modular agents demonstrate better generalization to test-time changes in both environment and agent structure.

DeepVir uses deep matrix factorization to predict antivirals for COVID-19.

problem Predicting effective antivirals for COVID-19 using known drug-virus associations.
method Graphical deep matrix factorization with HyPALM optimization.
result DeepVir outperforms state-of-the-art techniques in predicting antivirals for COVID-19.

This paper automates mining of COVID-19 scholarly articles using machine learning.

problem Time-consuming and impractical manual extraction of relevant COVID-19 research articles.
method Used machine learning approaches, specifically clustering and parallel one-class support vector machines (OCSVMs), on the CORD-19 dataset.
result Parallel OCSVMs outperform other methods for both original and reduced feature space.

Paper proposes neural networks for automatically naming assembly functions.

problem Automatically assigning names to assembly code functions.
method Formal definition of problem, baseline models (Seq2Seq, Transformer), fine-tuning neural networks.
result Neural networks can effectively predict function names in binaries, even outperforming state-of-the-art.

Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.

problem Designing effective external control protocols for self-assembly with high-resolution control.
method Investigated a multi-agent reinforcement learning approach, comparing fully decentralized and partially decentralized strategies.
result Partially decentralized approach outperforms fully decentralized in controlling self-assembly towards target structures.

This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.

problem Greedy testing strategies miss dormant virus areas, leading to inefficient use of testing resources.
method Develops efficient learning strategies based on policy iteration and look-ahead rules for a sequential learning-based resource allocation problem.
result Shows that the testing problem can be effectively managed using a partially observable MDP approach.