A Framework for Indoor Positioning Including Building Topology

In many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting. Research in this area has been faced with a number of challenges, leading to the proposal of a number of localisation algorithms, sampling strategies, benchmark datasets, and representations of building information. This proliferation made the modeling of the indoor positioning domain quite hard from both a theoretical and a practical point of view. In this paper, we propose a general and extensible framework, based on a relational database, that pairs fingerprints with building information. We show how the proposed system successfully deals with a number of problems that affect indoor positioning, supporting a large set of relevant tasks. The source code of the framework is available online, as well as an implementation of it, that provides an interactive open repository of indoor positioning data

Medical Information Extraction with Large Language Models

The increase in clinical text data following the adoption of electronic health records offers benefits for medical practice and introduces challenges in automatic data extraction. Since manual extraction is often inefficient and error-prone, with this work, we explore the use of open, small-scale, Large Language Models (LLMs) to automate and improve the extraction of medication and timeline data. With our experiments, we aim to assess the effectiveness of different prompting strategies –zero-shot, few-shots, and sequential prompting– on LLMs to generate a mixture of structured and unstructured information starting from a reference document. The results show that even a zero-shot learning approach can be sufficient to extract medication information with high precision. The main issues in generating the required information seem to be completeness and redundancy. However, prompt tuning alone seems to be sufficient to achieve good results using these LLMs, even in specific domains like the medical one. Besides medical information extraction, in this work, we address the problem of explainability, introducing a line-number referencing method to enhance transparency and trust in the generated results. Finally, to underscore the viability of applying these LLM-based solutions to medical information extraction, we deployed the developed pipelines within a demo application

Monitors that Learn from Failures: Pairing STL and Genetic Programming

In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behaviour of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behaviour of the considered system, encoded by means of formulas of Signal Temporal Logic (STL). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviours in an online setting, providing human-interpretable results

Lymnaea stagnalis as model for translational neuroscience research: from pond to bench

The purpose of this review is to illustrate how a reductionistic, but sophisticated, approach based on the use of a simple model system such as the pond snail Lymnaea stagnalis (L. stagnalis), might be useful to address fundamental questions in learning and memory. L. stagnalis, as a model, provides an interesting platform to investigate the dialog between the synapse and the nucleus and vice versa during memory and learning. More importantly, the "molecular actors" of the memory dialogue are well-conserved both across phylogenetic groups and learning paradigms, involving single- or multi-trials, aversion or reward, operant or classical conditioning. At the same time, this model could help to study how, where and when the memory dialog is impaired in stressful conditions and during aging and neurodegeneration in humans and thus offers new insights and targets in order to develop innovative therapies and technology for the treatment of a range of neurological and neurodegenerative disorders

Experimental observation of the Bogoliubov transformation for a Bose-Einstein condensed gas

Phonons with wavevector $q/\hbar$ were optically imprinted into a Bose-Einstein condensate. Their momentum distribution was analyzed using Bragg spectroscopy with a high momentum transfer. The wavefunction of the phonons was shown to be a superposition of +q and -q free particle momentum states, in agreement with the Bogoliubov quasiparticle picture.Comment: 4 pages, 3 figures, please take postscript version for the best version of Fig

The Excitation Spectrum of a Bose-Einstein Condensate

We report the first measurement of the excitation spectrum and the static structure factor of a Bose-Einstein condensate. The excitation spectrum displays a linear phonon regime, as well as a parabolic single-particle regime. The linear regime provides an upper limit for the superfluid critical velocity, by the Landau criterion. The excitation spectrum agrees well with the Bogoliubov spectrum, in the local density approximation. This agreement continues even for excitations close to the long-wavelength limit of the region of applicability of the approximation. Feynman's relation between the excitation spectrum and the static structure factor is verified, within an overall constant

How to measure the Bogoliubov quasiparticle amplitudes in a trapped condensate

We propose an experiment, based on two consecutive Bragg pulses, to measure the momentum distribution of quasiparticle excitations in a trapped Bose gas at low temperature. With the first pulse one generates a bunch of excitations carrying momentum $q$, whose Doppler line is measured by the second pulse. We show that this experiment can provide direct access to the amplitudes $u_{q}$ and $v_{q}$ characterizing the Bogoliubov transformations from particles to quasiparticles. We simulate the behavior of the nonuniform gas by numerically solving the time dependent Gross-Pitaevskii equation.Comment: 12 pages, 4 figures include