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We investigate the optimal investment-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques we convert the original optimization problem involving different filtrations, into an equivalent stochastic control problem under the observation filtration only, the so-called separated problem. The Markovian structure of the separated problem allows us to apply a classical approach to stochastic optimization based on the Hamilton-Jacobi-Bellman equation, and to provide explicit formulas for the value function and the optimal investment-reinsurance strategy. We finally discuss some comparisons between the optimal strategies pursued by a partially informed insurer and that followed by a fully informed insurer, and we evaluate the value of information using the idea of indifference pricing. These results are also supported by numerical experiments.

Direct numerical simulations (DNS) of rotating pipe flows up to $Re_{\tau} \approx 3000$ are carried out to investigate drag reduction effects associated with axial rotation, extending previous studies carried out at a modest Reynolds number (Orlandi & Fatica 1997; Orlandi & Ebstein 2000). The results show that the drag reduction, which we theoretically show to be equivalent to net power saving assuming no mechanical losses, monotonically increases as either the Reynolds number or the rotation number increases, proportionally to the inner-scaled rotational speed. Net drag reduction up to about $70\%$ is observed, while being far from flow relaminarisation. Scaling laws for the mean axial and azimuthal velocity are proposed, from which a predictive formula for the friction factor is derived. The formula can correctly represent the dependency of the friction factor on the Reynolds and rotation numbers, maintaining good accuracy for low-to-moderate rotation numbers. Examination of the turbulent structures highlights the role of rotation in widening and elongating the small-scale streaks, with subsequent suppression of sweeps and ejections. In the core part of the flow, clear weakening of large-scale turbulent motions is observed at high Reynolds numbers, with subsequent suppression of the outer-layer peak in the pre-multiplied spectra. The Fukagata-Iwamoto-Kasagi decomposition indicates that, consistent with a theoretically derived formula, the outer layer yields the largest contribution to drag reduction at increasingly high Reynolds numbers. In contrast, both the inner and the outer layers contribute to drag reduction as the rotation number increases.

The study of starburst and AGN feedback is crucial for understanding the regulation of star formation and the evolution of galaxies across cosmic time. Arp 220, the closest ultraluminous infrared galaxy (ULIRG), is in an advanced phase of a major merger with two distinct nuclei, and shows evidence of multi-phase (molecular, ionised, neutral) and multi-scale (from < 0.1 to > 5 kpc) outflows. Therefore, it represents an ideal system for investigating outflow mechanisms and feedback phenomena in detail. Using new JWST NIRSpec IFU observations, we investigate the spatially resolved gaseous (in both ionized and hot molecular phases) and stellar kinematics in the innermost 1 kpc. We decouple the different gas kinematic components through multi-Gaussian fitting, identifying distinct multi-phase outflows associated with the two nuclei, with velocities up to $\sim$ 1000 km/s. We compute the mass ($\sim 10^7$ M$_\odot$), mass outflow rate ($\sim 20$ M$_\odot$/yr) and energetics ($\dot E_{out}\sim 10^{42}$ erg/s) for each outflow, finding that the ionized and hot molecular outflowing gas contribute around 2-30% to the total mass and the energy of the outflows, as inferred from the combination of multi-wavelength information. We discuss the possible origin of the outflows, finding no compelling evidence to prefer a starburst or AGN driven scenario. Regardless of their nature, outflows in Arp 220 propagate in multiple directions from parsec to kiloparsec scales, potentially impacting a significant portion of the host galaxy. This contrasts with isolated systems where outflows typically follow a more collimated path, and do not affect the interstellar medium throughout the entire galaxy. This study highlights the importance of investigating merging systems with multi-wavelength facilities, including JWST/NIRSpec IFS, to obtain a comprehensive understanding of feedback mechanisms in galaxy evolution., Comment: Submitted to A&A, 20 figures

We investigate the optimal reinsurance problem in a risk model with jump clustering features. This modeling framework is inspired by the concept initially proposed in Dassios and Zhao (2011), combining Hawkes and Cox processes with shot noise intensity models. Specifically, these processes describe self-exciting and externally excited jumps in the claim arrival intensity, respectively. The insurer aims to maximize the expected exponential utility of terminal wealth for general reinsurance contracts and reinsurance premiums. We discuss two different methodologies: the classical stochastic control approach based on the Hamilton-Jacobi-Bellman (HJB) equation and a backward stochastic differential equation (BSDE) approach. In a Markovian setting, differently from the classical HJB-approach, the BSDE method enables us to solve the problem without imposing any requirements for regularity on the associated value function. We provide a Verification Theorem in terms of a suitable BSDE driven by a two-dimensional marked point process and we prove an existence result relaying on the theory developed in Papapantoleon et al. (2018) for stochastic Lipschitz generators. After discussing the optimal strategy for general reinsurance contracts and reinsurance premiums, we provide more explicit results in some relevant cases. Finally, we provide comparison results that highlight the heightened risk stemming from the self-exciting component in contrast to the externally-excited counterpart and discuss the the monotonicity property of the value function., Comment: 38 pages

We present a detailed study of the centre of NGC4654, a Milky Way-like spiral galaxy in the Virgo cluster that has been reported to host a double stellar nucleus, thus promising a rare view of ongoing star cluster infall into a galaxy nucleus. Analysing JWST NIRSpec integral-field spectroscopic data and Hubble Space Telescope imaging of the inner 330 $\times$ 330 pc, we find that the nucleus harbours in fact three massive star clusters. Maps of infrared emission lines from NIRSpec show different morphologies for the ionised and molecular gas components. The emission from molecular hydrogen gas is concentrated at the NSC location, while emission from hydrogen recombination lines is more extended beyond the central cluster. The velocity fields of both gas and stars indicate that the three clusters are part of a complicated dynamical system, with the NSC having an elevated velocity dispersion in line with its high stellar mass. To investigate the stellar populations of the three clusters in more detail, we use surface brightness modelling to measure their fluxes from ultraviolet to mid-infrared wavelengths and fit their spectral energy distributions (SEDs). Two of the clusters are UV-bright and well described by single stellar populations with young ages ($\sim$ 3 and 5 Myr) and low masses ($M_\ast \sim 4 \times 10^{4} - 10^{5} M_\odot$), whereas the central cluster is much more massive ($3 \times 10^7 M_\odot$), and cannot be fitted by a single stellar population. Instead, we find that a minor young population ($\sim$ 1 Myr) embedded in a dominant old population ($\sim$ 8 Gyr) is needed to explain its SED. Given its complex composition and the close proximity of two young star clusters that are likely to merge with it within a few hundred million years, we consider NGC4654 a unique laboratory to study NSC growth from both in-situ star formation and the infall of star clusters., Comment: Main text: 13 pages, 8 figures, accepted for publication in A&A

Arp 220 is the nearest ULIRG; it shows evidence of 100 pc-scale molecular outflows likely connected with galaxy-scale outflows traced by ionised and neutral gas. The two highly obscured nuclei of Arp 220 are the site of intense star formation, with extreme star-formation rate surface densities (~ 10^3 Msun/yr/kpc2). Despite extensive investigations searching for AGN activity in the Arp 220 nuclei, direct evidence remains elusive. We present JWST/NIRSpec IFS observations covering the 0.9 - 5.1 um wavelength range of the innermost (5''x4'', i.e. 1.8x1.5 kpc) regions of Arp 220. The primary goal is to investigate the potential presence of AGN signatures in the nuclear regions by analysing the spectra extracted from circular apertures of radius 55 pc (0.15'') around each of the two nuclei. We identify ~ 70 ionised and ~ 50 molecular emission lines in the nuclear spectra of Arp 220; we use recombination line ratios to measure optical extinctions in the range AV ~ 11 - 14 mag. High ionisation lines are not detected, except the [Mg IV] line at 4.49 um which we interpret as due to shocks rather than to AGN ionisation. We identify broadening and multiple kinematic components in the HI and H2 lines caused by outflows and shocks, with velocities up to ~ 550 km/s. Significantly higher velocities (up to ~ 900 km/s) are detected in the off-nuclear regions; however, they do not conclusively represent evidence for AGN activity. Even with the unprecedented sensitivity of JWST/NIRSpec IFS, achieving an unambiguous identification or exclusion of the presence of an AGN in the Arp 220 system remains challenging, because of its extreme dust obscuration., Comment: submitted to A&A, comments welcome

Modern software has been an integral part of everyday activities in many disciplines and application contexts. Introducing intelligent automation by leveraging artificial intelligence (AI) led to break-throughs in many fields. The effectiveness of AI can be attributed to several factors, among which is the increasing availability of data. Regulations such as the general data protection regulation (GDPR) in the European Union (EU) are introduced to ensure the protection of personal data. Software systems that collect, process, or share personal data are subject to compliance with such regulations. Developing compliant software depends heavily on addressing legal requirements stipulated in applicable regulations, a central activity in the requirements engineering (RE) phase of the software development process. RE is concerned with specifying and maintaining requirements of a system-to-be, including legal requirements. Legal agreements which describe the policies organizations implement for processing personal data can provide an additional source to regulations for eliciting legal requirements. In this chapter, we explore a variety of methods for analyzing legal requirements and exemplify them on GDPR. Specifically, we describe possible alternatives for creating machine-analyzable representations from regulations, survey the existing automated means for enabling compliance verification against regulations, and further reflect on the current challenges of legal requirements analysis.

Users rely on search engines for information in critical contexts, such as public health emergencies. Understanding how users evaluate the trustworthiness of search results is therefore essential. Research has identified rank and the presence of misinformation as factors impacting perceptions and click behavior in search. Here, we elaborate on these findings by measuring the effects of rank and misinformation, as well as warning banners, on the perceived trustworthiness of individual results in search. We conducted three online experiments (N=3196) using Covid-19-related queries to address this question. We show that although higher-ranked results are clicked more often, they are not more trusted. We also show that misinformation did not change trust in accurate results below it. However, a warning about unreliable sources backfired, decreasing trust in accurate information but not misinformation. This work addresses concerns about how people evaluate information in search, and illustrates the dangers of generic prevention approaches., Comment: 24 pages, 10 figures, 4 supplementary files

User reporting is an essential component of content moderation on many online platforms -- in particular, on end-to-end encrypted (E2EE) messaging platforms where platform operators cannot proactively inspect message contents. However, users' privacy concerns when considering reporting may impede the effectiveness of this strategy in regulating online harassment. In this paper, we conduct interviews with 16 users of E2EE platforms to understand users' mental models of how reporting works and their resultant privacy concerns and considerations surrounding reporting. We find that users expect platforms to store rich longitudinal reporting datasets, recognizing both their promise for better abuse mitigation and the privacy risk that platforms may exploit or fail to protect them. We also find that users have preconceptions about the respective capabilities and risks of moderators at the platform versus community level -- for instance, users trust platform moderators more to not abuse their power but think community moderators have more time to attend to reports. These considerations, along with perceived effectiveness of reporting and how to provide sufficient evidence while maintaining privacy, shape how users decide whether, to whom, and how much to report. We conclude with design implications for a more privacy-preserving reporting system on E2EE messaging platforms., Comment: accepted to SOUPS 2023

As part of the Digital Working Group (GTnum) #Scol_IA "Renewal of digital practices and creative uses of digital and AI" we are pleased to present the white paper "Teaching and learning in the era of Artificial Intelligence, Acculturation, integration and creative uses of AI in education". The white paper edited by Margarida Romero, Laurent Heiser and Alexandre Lepage aims to provide the various educational actors with a diversified perspective both on the issues of acculturation and training in AI and on the resources and feedback from the various research teams and organisations. of scientific culture in the French-speaking countries. A multidisciplinary approach makes it possible to consider the perspectives of researchers in computer science as well as those of education and training sciences, information and communication sciences and the expertise of teaching professionals. and scientific mediation., Comment: in French language

Electronics repair and service providers offer a range of services to computing device owners across North America -- from software installation to hardware repair. Device owners obtain these services and leave their device along with their access credentials at the mercy of technicians, which leads to privacy concerns for owners' personal data. We conduct a comprehensive four-part study to measure the state of privacy in the electronics repair industry. First, through a field study with 18 service providers, we uncover that most service providers do not have any privacy policy or controls to safeguard device owners' personal data from snooping by technicians. Second, we drop rigged devices for repair at 16 service providers and collect data on widespread privacy violations by technicians, including snooping on personal data, copying data off the device, and removing tracks of snooping activities. Third, we conduct an online survey (n=112) to collect data on customers' experiences when getting devices repaired. Fourth, we invite a subset of survey respondents (n=30) for semi-structured interviews to establish a deeper understanding of their experiences and identify potential solutions to curtail privacy violations by technicians. We apply our findings to discuss possible controls and actions different stakeholders and regulatory agencies should take to improve the state of privacy in the repair industry., Comment: This paper has been accepted to appear at the 44th IEEE Symposium on Security and Privacy (IEEE S&P 2023)