Working Papers/Reports
Recent Submissions

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 complexvalued vectors; (2) the design of Hierarchical Resonator Networks (HRN) to deal with the noncommutative nature ...Working Paper 

Multimodal system for recording individuallevel behaviors in songbird groups
(2022)Working Paper 
BiologicallyInspired 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 braininspired replay model (BIR) by building a biologicallyinspired conditional temporal variational autoencoder (BICTVAE), which instantiates a latent mixtureofGaussians for class representation. We investigate a novel continuallearningtogenerate (CL2Gen) scenario where the model ...Working Paper 

Fullduplex acoustic interaction system for cognitive experiments with cetaceans
(2022)Working Paper 

VoldKalman Filter Order tracking of Axle Box Accelerations for Railway Stiffness Assessment
(2022)arXivIntelligent datadriven 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 
HighDimensional 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 NPhardness of ...Working Paper 

Practical and scalable simulations of nonMarkovian 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 SARSCoV2 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 

A synthetic bispecific antibody capable of neutralizing SARSCoV2 Delta and Omicron
(2022)Working Paper 
Approaching the isoperimetric problem in H^m_C via the hyperbolic logconvex density conjecture
(2022)arXivWorking Paper 