Normal liquid He3 studied by path-integral Monte Carlo with a parametrized partition function

We compute the energy per particle of normal liquid 3 He in the temperature range 0.15–2 K using path-integral Monte Carlo simulations, leveraging a recently proposed method to overcome the sign problem—a long-standing challenge in many-body fermionic simulations. This approach is based on introducing a parameter ξ into the partition function, which allows a generalization from bosons (ξ=1) to fermions (ξ=−1). By simulating systems with ξ≥0, where the sign problem is absent, one can then extrapolate to the fermionic case at ξ=−1. Guided by an independent-particle model that uncovers nonanalytic behavior due to the superfluid transition, which is moderated by finite-size effects, we develop a tailored extrapolation strategy for liquid 3 He that departs from the extrapolation schemes shown to be accurate in those cases where quantum degeneracy effects are weak, and enables accurate results in the presence of Bose-Einstein condensation and superfluidity for ξ>0. Our approach extends the previously proposed framework and yields energy per particle values in good agreement with experimental data

Sensore di gas a stato solido e corrispondente procedimento di fabbricazione

Object of the invention The object of the present invention is to provide an improved solid-state gas sensor that allows achieving higher selectivity than known gas sensors, solving one or more of the technical problems mentioned above

Activation and characterization of Rb MEMS cells with an automatic system at wafer level

The push towards miniaturized and low-power quantum sensors demands reliable and mass-manufacturable atomic reservoirs. In this paper, we report on the implementation of an automatic system to activate and characterize a wafer of microfabricated Rb cells. The setup is composed of a motorized translation system jointly with two optical sources, a high-power one used for activating Rb pills and the other for spectroscopy purposes. The spectroscopy signal is analyzed in real-time to check the release of Rb during activation. Alternatively, the signal recognition can be used in post-production for screening the entire wafer. In this sense, the presented automated setup represents an effective tool to characterize the cell production in terms of Rb content and signal contrast, making a step towards mass production of devices based on miniaturized alkali vapor cells

LVG-SfM: Learning-Based View-Graph Generation for Robust on-the-Fly SfM

Structure from Motion (SfM) has been widely studied in many fields, such as computer vision, photogrammetry, robotics, etc. Recent advancements focus on improving the real-time performance of SfM, which is crucial for applications in augmented reality, mixed reality, robotics, etc. However, the robustness of real-time processing is still limited by outliers in the feature extraction and matching process, stemming from challenging scenes depicting objects with poor texture, repetitive structures, and symmetric objects, which can cause blunders in the view-graph. Focusing on these scenes, a Learning-based View-Graph generation method (LVG-SfM) is investigated and integrated into the on-the-fly SfM pipeline [43]. First, to provide a higher number of reliable matches and generate a more robust view-graph, a set of SoTA learning-based feature extraction and matching methods [19] are tested. Then, the spuriously incorrect two-view geometries generated from repetitive structures are removed from the view-graph with the help of SoTA learning-based disambiguation network - Doppelgangers [3]. Experimental results demonstrate that our LVG-SfM can successfully work on-the-fly on challenging ambiguous scenes with poor textures and repetitive structures, achieving correct scene reconstructions and robustifying SfM. Project website at: https://sygant.github.io/lvgsfm

Quantitative analysis of different SLAM algorithms for geo-monitoring in an underground test field

Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely. However, this task inherently exposes monitoring staff to hazardous environments, especially in underground settings. Since 2000s, robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping. However, only a few studies have conducted quantitative evaluations of these methods, and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring. In this study, a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed. This involves the development of an underground test field and some specific metrics, which allow detailed local accuracy analysis of point measurements, line segments, and areas using artificial targets. With this proposed methodology, a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR- and visual-based SLAM methods. The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner. The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates, cloud-to-cloud distances between the test and reference point cloud, normal vector, centre point coordinates and area of the planar objects. The results demonstrate that the HDL Graph SLAM achieves satisfactory precision, accuracy, and repeatability with a mean cloud-to-cloud distance of 0.12 m (with a standard deviation of 0.13 m) in an 80 m closed-loop measurement area. Although RTAB-Map exhibits better plane-capturing capabilities, the measurement results reveal instability and inaccuracies

Unified Planning: Modeling, manipulating and solving AI planning problems in Python

Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a feature-rich Python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users

The First Workshop on Multilingual Counterspeech Generation at COLING 2025: Overview of the Shared Task

This paper presents an overview of the Shared Task organized in the First Workshop on Multilingual Counterspeech Generation at COLING 2025. While interest in automatic approaches to Counterspeech generation has been steadily growing, the large majority of the published experimental work has been carried out for English. This is due to the scarcity of both non-English manually curated training data and to the crushing predominance of English in the generative Large Language Models (LLMs) ecosystem. The task’s goal is to promote and encourage research on Counterspeech generation in a multilingual setting (Basque, English, Italian, and Spanish) potentially leveraging background knowledge provided in the proposed dataset. The task attracted 11 participants, 9 of whom presented a paper describing their systems. Together with the task, we introduce a new multilingual counterspeech dataset with 2384 triplets of hate speech, counterspeech, and related background knowledge covering 4 languages. The dataset is available at: https://huggingface.co/datasets/LanD-FBK/ML_MTCONAN_KN