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Six years after the entry into force of the GDPR, European companies and organizations still have difficulties complying with it: the amount of fines issued by the European data protection authorities is continuously increasing. Personal data transfers are no exception. In this work we analyse the personal data transfers from more than 20000 Italian Public Administration (PA) entities to third countries. We developed "Minos", a user-friendly application which allows to navigate the web while recording HTTP requests. Then, we used the back-end of Minos to automate the analysis. We found that about 14% of the PAs websites transferred data out of the European Economic Area (EEA). This number is an underestimation because only visits to the home pages were object of the analysis. The top 3 destinations of the data transfers are Amazon, Google and Fonticons, accounting for about the 70% of the bad requests. The most recurrent services which are the object of the requests are cloud computing services and content delivery networks (CDNs). Our results highlight that, in Italy, a relevant portion of public administrations websites transfers personal data to non EEA countries. In terms of technology policy, these results stress the need for further incentives to improve the PA digital infrastructures. Finally, while working on refinements of Minos, the version here described is openly available on Zenodo: it can be helpful to a variety of actors (citizens, researchers, activists, policy makers) to increase awareness and enlarge the investigation., Comment: International Conference on Information Technology for Social Good (GoodIT '24), September 4-6, 2024, Bremen, Germany

Generative AI offers a simple, prompt-based alternative to fine-tuning smaller BERT-style LLMs for text classification tasks. This promises to eliminate the need for manually labeled training data and task-specific model training. However, it remains an open question whether tools like ChatGPT can deliver on this promise. In this paper, we show that smaller, fine-tuned LLMs (still) consistently and significantly outperform larger, zero-shot prompted models in text classification. We compare three major generative AI models (ChatGPT with GPT-3.5/GPT-4 and Claude Opus) with several fine-tuned LLMs across a diverse set of classification tasks (sentiment, approval/disapproval, emotions, party positions) and text categories (news, tweets, speeches). We find that fine-tuning with application-specific training data achieves superior performance in all cases. To make this approach more accessible to a broader audience, we provide an easy-to-use toolkit alongside this paper. Our toolkit, accompanied by non-technical step-by-step guidance, enables users to select and fine-tune BERT-like LLMs for any classification task with minimal technical and computational effort.

Our earlier laboratory measurements showed that low-velocity sand impacts release fine <5 {\mu}m dust from a Martian simulant soil. This dust will become airborne in the Martian atmosphere. Here, we extend this study by measuring aerodynamic properties of ejecta and characterizing deviations from the behavior of spherical, monolithic grains. We observe the settling of particles emitted as part of an impact splash. The sizes (20 to 280 {\mu}m) and sedimentation velocities (0.1 to 0.8 ms^{-1} ) of the particles are deduced from high-speed videos while the particles sediment under low ambient pressure of about 1 mbar. The particles regularly settle slower than expected, down to a factor of about 0.3. Using optical microscopy, the shape of the captured particles is characterized by simple axis ratios (longest/smallest), which show that the vast majority of particles are irregular but typically not too elongated, with axis ratios below 2 on average. Electron microscopy further reveals that the particles are typically porous aggregates, which is the most likely reason for the reduction of the sedimentation velocity. Due to the reduced bulk density, aggregates up to 10 {\mu}m in diameter should regularly be a part of the dust in the Martian atmosphere., Comment: 8 pages, 7 figures

We introduce and characterize the Multi-cluster Two-Wave (MTW) fading model, which generalizes \textit{both} the Durgin's Two-Wave with Diffuse Power (TWDP) and the $\kappa$-$\mu$ models under a common umbrella. The MTW model consists of an arbitrary number of clusters of waves each of which may include one or two dominant (specular) components. The chief probability functions of the MTW fading model are obtained, including the probability density function, the cumulative distribution function and the generalized moment-generating function. \textcolor{black}{The proposed model is empirically validated using channel measurements in the sub-THz band and} a number of applications are exemplified, including the outage probability in noise-limited and interference-limited scenarios and the energy detection probability. %Exact expressions for the outage capacity are also obtained. A composite Inverse Gamma (IG)/MTW model is also investigated, thus extending the proposed propagation model to include shadowing., Comment: This work has been submitted to the IEEE for publication

All possible phenotypes are not equally accessible to evolving populations. In fact, only phenotypes of large size, i.e. those resulting from many different genotypes, are found in populations of sequences, presumably because they are easier to discover and maintain. Genotypes that map to these phenotypes usually form mostly connected genotype networks that percolate the space of sequences, thus guaranteeing access to a large set of alternative phenotypes. Within a given environment, where specific phenotypic traits become relevant for adaptation, the replicative ability of a phenotype and its overall fitness (in competition experiments with alternative phenotypes) can be estimated. Two primary questions arise: how do phenotype size, reproductive capability and topology of the genotype network affect the fitness of a phenotype? And, assuming that evolution is only able to access large phenotypes, what is the range of unattainable fitness values? In order to address these questions, we quantify the adaptive advantage of phenotypes of varying size and spectral radius in a two-peak landscape. We derive analytical relationships between the three variables (size, topology, and replicative ability) which are then tested through analysis of genotype-phenotype maps and simulations of population dynamics on such maps. Finally, we analytically show that the fraction of attainable phenotypes decreases with the length of the genotype, though its absolute number increases. The fact that most phenotypes are not visible to evolution very likely forbids the attainment of the highest peak in the landscape. Nevertheless, our results indicate that the relative fitness loss due to this limited accessibility is largely inconsequential for adaptation., Comment: 25 pages, 10 figures, uses iopart.cls, iopart10.clo, iopart12.clo, iopams.sty, setstack.sty

In this article, a thorough characterization of the configuration composed by two concentric jets at a low Reynolds number is presented. The analysis comprises a layout with a wide range for the velocity ratio between the inner and outer jets, defined within the interval [0, 2], and also details the influence of the distance between jets, where the wall thicknesses separating the two jets is [0.5, 4]. Global linear stability analysis identifies the most significant modes driving the changes in the flow dynamics. The neutral lines revealing the critical Reynolds number connected to the presence of the main (steady and unsteady) flow bifurcations, which are presented by global azimuthal modes, show the high complexity of the problem under study, where hysteresis and other types of complex cycles are pointed out. Finally, the mode interaction is analysed, highlighting the presence of travelling waves emerging from the interaction of steady states, and the existence of robust heteroclinic cycles that are asymptotically stable. The high level of detail in the results presented, makes this work as a reference for future research development in the field of concentric jets.

Emission of dust up to a few micrometer in size by impacts of sand grains during saltation is thought to be one source of dust within the Martian atmosphere. To study this dust fraction, we carried out laboratory impact experiments. Small numbers of particles of about 200\textmu{}m in diameter impacted a simulated Martian soil (bimodal \textit{Mars Global Simulant}). Impacts occurred at angles of $\sim 18^\circ$ in vacuum with an impact speed of $\sim 1 \rm m/s$. Ejected dust was captured on adjacent microscope slides and the emitted particle size distribution (PSD) was found to be related to the soil PSD. We find that the ejection of clay sized dust gets increasingly harder the smaller these grains are. However, in spite of strong cohesive forces, individual impacts emit dust of 1\textmu{}m and less, i.e. dust in the size range that can be suspended in the Martian atmosphere. More generally, the probability of ejecting dust of a given size can be characterized by a power law in the size range between 0.5\textmu{}m and 5\textmu{}m (diameter)., Comment: 10 pages, 7 figures; Published by the American Astronomical Society

Objective: To conduct a proof-of-concept study of the detection of two synthetic models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polarimetric imaging. Methods: Two SARS-CoV-2 models were prepared as engineered lentiviruses pseudotyped with the G protein of the vesicular stomatitis virus, and with the characteristic Spike protein of SARS-CoV-2. Samples were preparations in two biofluids (saline solution and artificial saliva), in four concentrations, and deposited as 5-{\mu}L droplets on a supporting plate. The angles of maximal degree of linear polarization (DLP) of light diffusely scattered from dry residues were determined using Mueller polarimetry of 87 samples at 405 nm and 514 nm. A polarimetric camera was used for simultaneous imaging of several samples under 380-420 nm illumination at angles similar to those of maximal DLP. A per-pixel image analysis included quantification and combination of polarization feature descriptors in other 475 samples. Results: The angles (from sample surface) of maximal DLP were 3 degrees for 405 nm and 6 degrees for 514 nm. Similar viral particles that differ only in the characteristic spike protein of the SARS-CoV-2, their corresponding negative controls, fluids, and the sample holder were discerned from polarimetric image analysis at 10-degree and 15-degree configurations. Conclusion: Polarimetric imaging in the visible spectrum has the potential for non-contact, reagent-free detection of viruses in multiple dry fluid residues simultaneously. Further analysis including real SARS-CoV-2 in human samples -- particularly, fresh saliva -- are required. Significance: Polarimetric imaging under visible light could contribute to fast, cost-effective screening of SARS-CoV-2 and other pathogens.

This paper addresses the problem of guidance and control of underwater vehicles. A multi-level control strategy is used to determine (1) outer-loop path-following commands and (2) inner-loop actuation commands. Specifically, a line-of-sight path-following algorithm is used to guide the vehicle along a three-dimensional path, and an $\mathcal{L}_1$ adaptive control algorithm is used to determine the low-level rudder commands to accomplish path following. The performance bounds of these outer- and inner-loop control algorithms are presented. Numerical results obtained using a physics-based Simulink model are used to aid in visualization of the control algorithm's performance.

While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we present an AI-based Brain-Computer Interface (BCI) in which humans and machines interact to express feelings artistically. This system and its production of images give opportunities to reflect on the complexities and range of human emotions and their expressions. In this discussion, we seek to understand the dynamics of this interaction to reach better co-existence in fairness, inclusion, and aesthetics., Comment: Accepted to the Politics of the Machines conference 2021 (POM Berlin 2021)