Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information

This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution that correlates the two. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. By considering bounds to the misclassification probability associated with the recovery of the underlying signal label, and bounds to the reconstruction error associated with the recovery of the signal of interest itself, we then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low rank. These conditions, which are reminiscent of the well-known Slepian–Wolf and Wyner–Ziv conditions, are the function of the number of linear features extracted from signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive the expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow the identification of operational regimes, where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications. In particular, we report state-of-the-art results in compressive hyperspectral imaging applications, where the accompanying side information is a conventional digital photograph.This work was supported in part by the (PIDDAC) for Future Health/Faculdade de Engenharia da Universidade do Porto under Grant NORTE-07-0124-FEDER-000068, funded by the Fundo Europeu de Desenvolvimento Regional through the Programa Operacional do Norte, in part by the National Funds, through FCT/MEC (PIDDAC), in part by the Royal Society International Exchanges Scheme under Grant IE120996, in part by AFOSR, in part by ARO, in part by DARPA, in part by DOE, in part by NGA, and in part by ONR. F. Renna was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie under Grant 655282. M. R. D. Rodrigues was supported by EPSRC under Grant EP/K033166/1

Deep learning based pulse shape discrimination for germanium detectors

Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based method to recognize one of the most abundant classes of background events in these experiments. By combining a neural network for feature extraction with a smaller classification network, our method can be trained with only a small number of labeled events. To validate our method, we use signals from a broad-energy germanium detector irradiated with a $^{228}$Th gamma source. We find that it matches the performance of state-of-the-art algorithms commonly used for this detector type. However, it requires less tuning and calibration and shows potential to identify certain types of background events missed by other methods.Comment: Published in Eur. Phys. J. C. 9 pages, 10 figures, 3 table

Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime

Genome organization and interaction with capsid protein in a multipartite RNA virus.

We report the asymmetric reconstruction of the single-stranded RNA (ssRNA) content in one of the three otherwise identical virions of a multipartite RNA virus, brome mosaic virus (BMV). We exploit a sample consisting exclusively of particles with the same RNA content-specifically, RNAs 3 and 4-assembled in planta by agrobacterium-mediated transient expression. We find that the interior of the particle is nearly empty, with most of the RNA genome situated at the capsid shell. However, this density is disordered in the sense that the RNA is not associated with any particular structure but rather, with an ensemble of secondary/tertiary structures that interact with the capsid protein. Our results illustrate a fundamental difference between the ssRNA organization in the multipartite BMV viral capsid and the monopartite bacteriophages MS2 and Qβ for which a dominant RNA conformation is found inside the assembled viral capsids, with RNA density conserved even at the center of the particle. This can be understood in the context of the differing demands on their respective lifecycles: BMV must package separately each of several different RNA molecules and has been shown to replicate and package them in isolated, membrane-bound, cytoplasmic complexes, whereas the bacteriophages exploit sequence-specific "packaging signals" throughout the viral RNA to package their monopartite genomes