Working Papers/Reports
Recent Submissions
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Neuromorphic Visual Scene Understanding with Resonator Networks
(2022)Inferring the position of objects and their rigid transformations is still an open problem in visual scene understanding. Here we propose a neuromorphic solution that utilizes an efficient factorization network based on three key concepts: (1) a computational framework based on Vector Symbolic Architectures (VSA) with complex-valued vectors; (2) the design of Hierarchical Resonator Networks (HRN) to deal with the non-commutative nature ...Working Paper -
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Multimodal system for recording individual-level behaviors in songbird groups
(2022)Working Paper -
Biologically-Inspired Continual Learning of Human Motion Sequences
(2022)This work proposes a model for continual learning on tasks involving temporal sequences, specifically, human motions. It improves on a recently proposed brain-inspired replay model (BI-R) by building a biologically-inspired conditional temporal variational autoencoder (BI-CTVAE), which instantiates a latent mixture-of-Gaussians for class representation. We investigate a novel continual-learning-to-generate (CL2Gen) scenario where the model ...Working Paper -
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Full-duplex acoustic interaction system for cognitive experiments with cetaceans
(2022)Working Paper -
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Vold-Kalman Filter Order tracking of Axle Box Accelerations for Railway Stiffness Assessment
(2022)arXivIntelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of the railway infrastructure in the face of increasing demands on cost and efficiency. Numerous studies have shown that the track stiffness is one of the main parameters influencing the evolution of degradation that drives maintenance processes. As such, the measurement of track stiffness is fundamental for characterizing ...Working Paper -
Does international “offshoring” of environmental impacts reflect consumer NIMBYism?
(2022)Working Paper -
High-Dimensional Inference in Bayesian Networks
(2021)Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables. While inference of the marginal probability distribution is crucial for various problems in machine learning and statistics, its exact computation is generally not feasible for categorical variables in Bayesian networks due to the NP-hardness of ...Working Paper -
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Practical and scalable simulations of non-Markovian stochastic processes
(2022)Discrete stochastic processes are widespread in natural systems with many applications across physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena. However, most simulation algorithms assume the system dynamics are memoryless (Markovian assumption), ...Working Paper -
Tracking SARS-CoV-2 genomic variants in wastewater sequencing data with<i>LolliPop</i>
(2022)Working Paper -
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
(2021)The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based ...Working Paper -
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A synthetic bispecific antibody capable of neutralizing SARS-CoV-2 Delta and Omicron
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Approaching the isoperimetric problem in H^m_C via the hyperbolic log-convex density conjecture
(2022)arXivWorking Paper -