Diffuse ultrasound computed tomography.

An alternative approach to acquire transmission travel time data is proposed, exploiting the geometry of devices commonly used in ultrasound computed tomography for medical imaging or non-destructive testing with ultrasonic waves. The intent is to (i) shorten acquisition time for devices with a large number of emitters, (ii) to eliminate the calibration step, and (iii) to suppress instrument noise. Inspired by seismic ambient field interferometry, the method rests on the active excitation of diffuse ultrasonic wavefields and the extraction of deterministic travel time information by inter-station correlation. To reduce stochastic errors and accelerate convergence, ensemble interferograms are obtained by phase-weighted stacking of observed and computed correlograms, generated with identical realizations of random sources. Mimicking an imaging setup, the accuracy of the travel time measurements as a function of the number of emitters and random realizations can be assessed both analytically and with spectral-element simulations for phantoms mimicking the model parameter distribution. The results warrant tomographic reconstructions with straight- or bent-ray approaches, where the effect of inherent stochastic fluctuations can be made significantly smaller than the effect of subjective choices on regularisation. This work constitutes a first conceptual study and a necessary prelude to future implementations.

Semi-empirical Analysis of Complex ITC Data from Protein-Surfactant Interactions.

Isothermal titration calorimetry (ITC) is a widely used method to determine binding affinities and thermodynamics in ligand-receptor interactions, but it also has the capability of providing detailed information on much more complex events. However, the lack of available methods to analyze ITC data is limiting the use of the technique in such multifaceted cases. Here, we present the software ANISPROU. Through a semi-empirical approach that allows for extraction of quantitative information from complex ITC data, ANISPROU solves an inverse problem where three parameters describing a set of predefined functions must be found. In analogy to strategies adopted in other scientific fields, such as geophysics, imaging, and many others, it employs an optimization algorithm which minimizes the difference between calculated and experimental data. In contrast to the existing methods, ANISPROU provides automated and objective analysis of ITC data on sodium dodecyl sulfate (SDS)-induced protein unfolding, and in addition, more information can be extracted from the data. Here, data series on SDS-mediated protein unfolding is analyzed, and binding isotherms and thermodynamic information on the unfolding events are extracted. The obtained binding isotherms as well as the enthalpy of different events are similar to those obtained using the existing manual methods, but our methodology ensures a more robust result, as the entire data set is used instead of single data points. We foresee that ANISPROU will be useful in other cases with complex enthalpograms, for example, in cases with coupled interactions in biomolecular, polymeric, and amphiphilic systems including cases where both structural changes and interactions occur simultaneously.

Guided Zoom: Zooming into Network Evidence to Refine Fine-Grained Model Decisions.

In state-of-the-art deep single-label classification models, the top- k (k=2,3,4, ...) accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. Exploiting the information provided in the top k predicted classes boosts the final prediction of a model. We propose Guided Zoom, a novel way in which explainability could be used to improve model performance. We do so by making sure the model has "the right reasons" for a prediction. The reason/evidence upon which a deep neural network makes a prediction is defined to be the grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable the evidence used to make each of the top- k predictions is. Test time evidence is deemed reasonable if it is coherent with evidence used to make similar correct decisions at training time. This leads to better informed predictions. We explore a variety of grounding techniques and study their complementarity for computing evidence. We show that Guided Zoom results in an improvement of a model's classification accuracy and achieves state-of-the-art classification performance on four fine-grained classification datasets. Our code is available at https://github.com/andreazuna89/Guided-Zoom.

Hamiltonian Nullspace Shuttles.

We present a method to explore the effective nullspace of nonlinear inverse problems without Monte Carlo sampling. This is based on the construction of an artificial Hamiltonian system where a model is treated as a high-dimensional particle. Depending on its initial momentum and mass matrix, the particle evolves along a trajectory that traverses the effective nullspace, thereby producing a series of alternative models that are consistent with observations and their uncertainties. Variants of the nullspace shuttle enable hypothesis testing, for example, by adding features or by producing smoother or rougher models. Furthermore, the Hamiltonian nullspace shuttle can serve as a tunable hybrid between deterministic and probabilistic inversion methods: Choosing random initial momenta, it resembles Hamiltonian Monte Carlo; requiring misfits to decrease along a trajectory, it transforms into gradient descent. We illustrate the concept with a low-dimensional toy example and with high-dimensional nonlinear inversions of seismic traveltimes and magnetic data, respectively.

Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature.

A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed. A likelihood function is derived from this map and fed to a particle filter yielding the target location estimation on the acoustic image. The method is tested on two real environments, addressing both speaker and vehicle tracking. The comparison with a couple of trackers, relying on the acoustic map only, shows a sharp improvement in performance, paving the way to the application of audio tracking in real challenging environments.