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We systematically investigate the interplay of disorder and space-time heterogeneous collisional noise in shaping the transport dynamics of an anisotropic XXZ spin chain. Using stochastic collision models to simulate interaction with the environment, we explore the localization-delocalization transitions across regimes with single and multiple excitations. We find that space homogeneous collisions occurring at low rates favor the shaping of regions where the localization degree sets in the form of subsequent plateaus at a rate and duration universally scaling with the collision rate. We also find that interactions among the excitations favor this process even for tiniest levels of disorder. Our findings can be applied to design stroboscopic protocols where sequences of transport and localization can be tailored. We establish relevant connections to noise-engineering of quantum devices in noisy intermediate-scale quantum simulators platforms, and to realistic biological systems where noise and disorder coexist., Comment: 28 pages, 7 figures

A core component of a successful artificial general intelligence would be the rapid creation and manipulation of grounded compositional abstractions and the demonstration of expertise in the family of recursive hierarchical syntactic objects necessary for the creative use of human language. We evaluated the recently released o3 model (OpenAI; o3-mini-high) and discovered that while it succeeds on some basic linguistic tests relying on linear, surface statistics (e.g., the Strawberry Test), it fails to generalize basic phrase structure rules; it fails with comparative sentences involving semantically illegal cardinality comparisons ('Escher sentences'); its fails to correctly rate and explain acceptability dynamics; and it fails to distinguish between instructions to generate unacceptable semantic vs. unacceptable syntactic outputs. When tasked with generating simple violations of grammatical rules, it is seemingly incapable of representing multiple parses to evaluate against various possible semantic interpretations. In stark contrast to many recent claims that artificial language models are on the verge of replacing the field of linguistics, our results suggest not only that deep learning is hitting a wall with respect to compositionality (Marcus 2022), but that it is hitting [a [stubbornly [resilient wall]]] that cannot readily be surmounted to reach human-like compositional reasoning simply through more compute.

Conventional decision-support systems, primarily based on supervised learning, focus on outcome prediction models to recommend actions. However, they often fail to account for the complexities of multi-actor environments, where diverse and potentially conflicting stakeholder preferences must be balanced. In this paper, we propose a novel participatory framework that redefines decision-making as a multi-stakeholder optimization problem, capturing each actor's preferences through context-dependent reward functions. Our framework leverages $k$-fold cross-validation to fine-tune user-provided outcome prediction models and evaluate decision strategies, including compromise functions mediating stakeholder trade-offs. We introduce a synthetic scoring mechanism that exploits user-defined preferences across multiple metrics to rank decision-making strategies and identify the optimal decision-maker. The selected decision-maker can then be used to generate actionable recommendations for new data. We validate our framework using two real-world use cases, demonstrating its ability to deliver recommendations that effectively balance multiple metrics, achieving results that are often beyond the scope of purely prediction-based methods. Ablation studies demonstrate that our framework, with its modular, model-agnostic, and inherently transparent design, integrates seamlessly with various predictive models, reward structures, evaluation metrics, and sample sizes, making it particularly suited for complex, high-stakes decision-making contexts.

After a successful kick-off meeting in 2021. two workshops in 2022 and 2023 on the future Global Cosmic-Ray Observatory (GCOS) focused mainly on a straw man design of the detector and science possibilities for astro- and particle physics. About 100 participants gathered for in-person and hybrid panel discussions. In this report, we summarize these discussions, present a preliminary straw-man design for GCOS and collect short write-ups of the flash talks given during the focus sessions., Comment: 48 pages, 27 figures

In recent years, numerical simulations have become indispensable for addressing complex astrophysical problems. The MagnetoHydroDynamics (MHD) framework represents a key tool for investigating the dynamical evolution of astrophysical plasmas, which are described as a set of partial differential equations that enforce the conservation of mass, momentum, and energy, along with Maxwell's equation for the evolution of the electromagnetic fields. Due to the high nonlinearity of the MHD equations (regardless of their specifications, e.g., classical/relativistic or ideal/resistive), a general analytical solution is precluded, making the numerical approach crucial. Numerical simulations usually end up producing large sets of data files and their scientific analysis leans on dedicated software designed for data visualization. However, in order to encompass all of the code output features, specialized tools focusing on the numerical code may represent a more versatile and built-in tool. Here, we present PyPLUTO, a Python package tailored for efficient loading, manipulation, and visualization of outputs produced with the PLUTO code (Mignone et al., 2007; Mignone et al., 2012). PyPLUTO uses memory mapping to optimize data loading and provides general routines for data manipulation and visualization. PyPLUTO also supports the particle modules of the PLUTO code, enabling users to load and visualize particles, such as cosmic rays (Mignone et al., 2018), Lagrangian (Vaidya et al., 2018), or dust (Mignone et al., 2019) particles, from hybrid simulations. A dedicated Graphical User Interface (GUI) simplifies the generation of single-subplot figures, making PyPLUTO a powerful yet user-friendly toolkit for astrophysical data analysis., Comment: 9 pages, 3 figures. Submitted to JOSS

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Measurements of the products of UHECR interactions with the Earth's atmosphere, as obtained in Extended Air Shower experiments, offer important information concerning hadronic interactions, which for some aspects overlaps and for many others complements the information extracted by measurements of collisions at human-made accelerators. In this contribution I discuss some of the constraints that the UHE astroparticle and accelerator fields exercise one over each other, emphasizing the importance of further new measurements, through new experiments or observations, in both fields., Comment: 8 pages, 1 figure, invited talk at the 13th Cosmic-Ray International Studies and Multi-messenger Astroparticle Conference (CRIS-MAC 2024), Trapani, Italy, June 17 - 21, 2024

The collection of updated data on social contact patterns following the COVID-19 pandemic disruptions is crucial for future epidemiological assessments and evaluating non-pharmaceutical interventions (NPIs) based on physical distancing. We conducted two waves of an online survey in March 2022 and March 2023 in Italy, gathering data from a representative population sample on direct (verbal/physical interactions) and indirect (prolonged co-location in indoor spaces) contacts. Using a generalized linear mixed model, we examined determinants of individuals' total social contacts and evaluated the potential impact of work-from-home and distance learning on the transmissibility of respiratory pathogens. In-person attendance at work or school emerged as a primary driver of social contacts. Adults attending in person reported a mean of 1.69 (95% CI: 1.56-1.84) times the contacts of those staying home; among children and adolescents, this ratio increased to 2.38 (95% CI: 1.98-2.87). We estimated that suspending all non-essential work alone would marginally reduce transmissibility. However, combining distance learning for all education levels with work-from-home policies could decrease transmissibility by up to 23.7% (95% CI: 18.2%-29.0%). Extending these measures to early childcare services would yield only minimal additional benefits. These results provide useful data for modelling the transmission of respiratory pathogens in Italy after the end of the COVID-19 emergency. They also provide insights into the potential epidemiological effectiveness of social distancing interventions targeting work and school attendance, supporting considerations on the balance between the expected benefits and their heavy societal costs., Comment: 10 pages, 2 tables, 3 figures

Recently, the Large High-Altitude Air Shower Observatory (LHAASO) collaboration has obtained a measurement of the gamma-ray diffuse emission in the ultra-high energy range, $10-10^3$ TeV after masking the contribution of known sources. The measurement appears to be 2-3 times higher than the gamma-ray signal expected from the hadronic interactions of diffuse cosmic rays with the interstellar medium, potentially suggesting a contribution from unresolved sources. However, estimates of the diffuse emission are affected by large uncertainties that must be accounted for. In this work, we calculate the hadronic gamma-ray diffuse emission including uncertainties in the gas content of the Galactic disk, in the energy and spatial distribution of cosmic rays as well as in the hadronic interaction cross-section. We show that the LHAASO data above $\sim 30$ TeV are consistent with the gamma-ray diffuse emission model when all these uncertainties are taken into account. This implies that, with the current data in this energy range, there is no need to invoke a cosmic ray spectral variation toward the Galactic center, nor a dominant contribution from unresolved sources.

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butylamines. Through post-synthesis ligand exchange, we functionalize the nanocrystals with butylamine ligands while maintaining their intrinsic properties. In solution, butylamine-capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern-Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting mixed static and sphere-of-action quenching interactions. Oleylamine-capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor-acceptor interactions, which are discussed in terms of trap state modification and electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine-capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length., Comment: 30 pages, 7 figures

Low-metallicity environments are subject to inefficient cooling. They also have low dust-to-gas ratios and therefore less efficient photoelectric (PE) heating than in solar-neighbourhood conditions, where PE heating is one of the most important heating processes in the warm neutral interstellar medium (ISM). We perform magneto-hydrodynamic simulations of stratified ISM patches with a gas metallicity of 0.02 Z$_\odot$ as part of the SILCC project. The simulations include non-equilibrium chemistry, heating, and cooling of the low-temperature ISM as well as anisotropic cosmic ray (CR) transport, and stellar tracks. We include stellar feedback in the form of far-UV and ionising (FUV and EUV) radiation, massive star winds, supernovae, and CR injection. From the local CR energy density, we compute a CR heating rate that is variable in space and time. In this way, we can compare the relative impact of PE and CR heating on the metal-poor ISM and find that CR heating can dominate over PE heating. Models with a uniform CR ionisation rate suppress or severely delay star formation, since they provide a larger amount of energy to the ISM due to CR heating. Models with a variable CR ionisation rate form stars predominantly in pristine regions with low PE heating and CR ionisation rates where the metal-poor gas is able to cool efficiently. Because of the low metallicity, the amount of formed stars in all runs is not enough to trigger outflows of gas from the mid-plane., Comment: 18 pages, 13 figures, accepted by MNRAS

Protoplanetary disks exhibit a rich variety of substructure in millimeter continuum emission, often attributed to unseen planets. As these planets carve gaps in the gas, dust particles can accumulate in the resulting pressure bumps, forming bright features in the dust continuum. We investigate the role of dust dynamics in the gap-opening process with 2D radiation hydrodynamics simulations of planet--disk interaction and a two-population dust component modeled as a pressureless fluid. We consider the opacity feedback and backreaction due to drag forces as mm grains accumulate in pressure bumps at different stages of dust growth. We find that dust dynamics can significantly affect the resulting substructure driven by the quasi-thermal-mass planet with $M_p/M_\star=10^{-4}$. Opacity feedback causes nonaxisymmetric features to become more compact in azimuth, whereas the drag-induced backreaction tends to dissolve nonaxisymmetries. For our fiducial model, this results in multiple concentric rings of dust rather than the expected vortices and corotating dust clumps found in models without dust feedback. A higher coagulation fraction disproportionately enhances the effect of dust opacity feedback, favoring the formation of crescents rather than rings. Our results suggest that turbulent diffusion is not always necessary to explain the rarity of observed nonaxisymmetric features, and that incorporating dust dynamics is vital for interpreting the observed substructure in protoplanetary disks. We also describe and test the implementation of the publicly-available dust fluid module in the PLUTO code., Comment: 16 pages, 17 figures, 2 tables; accepted in MNRAS

Finding a quantum battery model that displays a genuine quantum advantage, while being prone to experimental fabrication, is an extremely challenging task. In this Letter we propose a deceptively simple quantum battery model that displays a genuine quantum advantage, saturating the quantum speed limit. It consists of two harmonic oscillators (the charger and the battery), coupled during the non-equilibrium charging dynamics by a non-linear interaction. We first present the model, then certify the genuine quantum advantage, and finally briefly discuss how the battery can be fabricated through the use of superconducting circuits., Comment: 7 pages, 1 figure

We present a detailed analysis of the internal dynamics of multiple stellar populations (MPs) in 28 Galactic Globular Clusters (GCs) across a wide field of view, extending from the innermost regions to the clusters' outskirts. Using astro-photometric catalogs from ground-based observations, Gaia, and the Hubble Space Telescope (HST), we identify first- (1P) and second-population (2P) stars, and study the internal dynamics of MPs using high-precision Gaia DR3 and HST proper motions. Our results reveal that while the 1P transitions from isotropy to slight tangential anisotropy toward the outer regions, 2P stars become increasingly radially anisotropic beyond the half-light radius. We also explore the connection between the dynamics of MPs and the clusters' structural and dynamical properties, finding statistically significant differences in the anisotropy profiles of dynamically young and non-relaxed clusters, particularly beyond the 1-2 half-light radii. In these regions, 1P stars transition from isotropic to slightly tangentially anisotropic motion, while 2P stars become more radially anisotropic. In contrast, dynamically older clusters, with mixed MPs, exhibit weaker relative differences. Furthermore, clusters with orbits closer to the Galactic center exhibit larger dynamical differences between 1P and 2P stars than those with larger peri-Galactic radii. These findings are consistent with a scenario where 2P stars form in a more centrally concentrated environment, where the interaction with the Milky Way tidal field plays a crucial role in the dynamical evolution of MPs, especially of 1P., Comment: 19 pages, 13 figures (4 pages and 9 figures in appendix). Published in MNRAS, comments welcome

Let $\mathcal{F}_n(X;G)$ denote the set of number fields of degree $n$ with absolute discriminant no larger than $X$ and Galois group $G$. This set is known to be finite for any finite permutation group $G$ and $X \geq 1$. In this paper, we give a lower bound for the cases $G=\text{GL}_2(\mathbb{F}_\ell), \; \text{PGL}_2(\mathbb{F}_\ell)$ for primes $\ell \geq 13$. We also provide a method to compute lower bounds for any permutation representations of these groups.

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

A time-efficient and comprehensive verification is a fundamental part of the design process for modern computing platforms, and it becomes ever more important and critical to optimize as the latter get ever more complex. SupeRFIVe is a methodology for the functional verification of superscalar processors that leverages an instruction set simulator to validate their correctness according to a simulation-based approach, interfacing a testbench for the design under test with the instruction set simulator by means of socket communication. We demonstrate the effectiveness of the SupeRFIVe methodology by applying it to verify the functional correctness of a RISC-V dual-issue superscalar CPU, leveraging the state-of-the-art RISC-V instruction set simulator Spike and executing a set of benchmark applications from the open literature., Comment: 4 pages, 3 figures

Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification or segmentation. Inspired by this, we introduce Theia, a vision foundation model for robot learning that distills multiple off-the-shelf vision foundation models trained on varied vision tasks. Theia's rich visual representations encode diverse visual knowledge, enhancing downstream robot learning. Extensive experiments demonstrate that Theia outperforms its teacher models and prior robot learning models using less training data and smaller model sizes. Additionally, we quantify the quality of pre-trained visual representations and hypothesize that higher entropy in feature norm distributions leads to improved robot learning performance. Code, models, and demo are available at https://theia.theaiinstitute.com., Comment: CoRL 2024

In this article we discuss a peculiar regime of Compton Scattering that assures the maximum transfer of energy and momentum from free electrons propagating in vacuum to the scattered photons. We name this regime Full Inverse Compton Scattering (FICS) because it is characterized by the maximum and full energy loss of the electrons in collision with photons: up to 100 % of the electron kinetic energy is indeed transferred to the photon. In the case of relativistic electrons, characterized by a large Lorentz factor (gamma >> 1), FICS regime corresponds to an incident photon energy equal to mec^2/2 . We interpret such an astonishing result as FICS being the time reversal of direct Compton Scattering of very energetic photons (of energy much greater than mec2) onto atomic electrons. Although the cross section of Compton scattering is decreasing with the energy of the incident photon, making the process less probable with respect to other reactions (pair production, nuclear reactions, etc) when high energetic photons are bombarding a target, the kinematics straightforwardly implies that the back-scattered photons would have an energy reaching asymptotically me^2c^2 . FICS is instead the unique suitable working point in Compton scattering for achieving the total transfer of (kinetic) energy exactly from the electron to the photon. Experiencing transitions from the initial momentum to zero in the laboratory system, in FICS the electron is also subject to very large negative acceleration; this fact can lead to possible experiments of sensing the Unruh temperature and related photon bath. On the other side of the energy dynamic range, low relativistic electrons can be completely stopped by moderate energy photons (tens of keV), leading to full exchange of temperature between electron clouds and photon baths.

We present a finite-volume, genuinely 4th-order accurate numerical method for solving the equations of resistive relativistic magnetohydrodynamics (Res-RMHD) in Cartesian coordinates. In our formulation, the magnetic field is evolved in time in terms of face-average values via the constrained-transport method while the remaining variables (density, momentum, energy and electric fields) are advanced as cell volume-averages. Spatial accuracy employs 5th-order accurate WENO-Z reconstruction from point values (as described in a companion paper) to obtain left and right states at zone interfaces. Explicit flux evaluation is carried out by solving a Riemann problem at cell interfaces, using the Maxwell-Harten-Lax-van Leer with contact wave resolution (MHLLC). Time stepping is based on the implicit-explicit (IMEX) Runge-Kutta (RK) methods, of which we consider both the 3rd-order strong stability preserving SSP3(4,3,3) and a recent 4th-order additive RK scheme, to cope with the stiffness introduced by the source term in Ampere's law. Numerical benchmarks are presented in order to assess the accuracy and robustness of our implementation., Comment: 17 pages, 8 figures - Accepted 2024 July 6. Received 2024 July 3; in original form 2024 March 19

For nearly a decade, disinformation has dominated social debates, with its harmful impacts growing more evident. Episodes like the January 6 United States Capitol attack and the Rohingya genocide exemplify how this phenomenon has been weaponized. While considerable attention has been paid to its impact on societal discourse and minority persecution, there remains a gap in analyzing its role as a malicious hybrid tool targeting critical infrastructures. This article addresses this gap by presenting three case studies: a hypothetical scenario involving the electric grid, an attack on traffic management, and XZ Utils backdoor. Additionally, the study undertakes a criminological analysis to comprehend the criminal profiles driving such attacks, while also assessing their implications from a human rights perspective. The research findings highlight the necessity for comprehensive mitigation strategies encompassing technical solutions and crime prevention measures in order to safeguard critical infrastructures against these emerging threats., Comment: This paper was sent to CPS4CIP 2024

We revisit the kinematics of Compton Scattering (electron-photon interactions producing electrons and photons in the exit channel) covering the full range of energy/momenta distribution between the two colliding particles, with a dedicated view to statistical properties of secondary beams that are generated in beam-beam collisions. Starting from the Thomson inverse scattering, where electrons do not recoil and photons are back-scattered to higher energies by a Lorentz boost effect (factor $4\gamma^2$), we analyze three transition points, separating four regions. These are in sequence, given by increasing the electron recoil (numbers are for transition points, letters for regions): a) Thomson back-scattering, 1) equal sharing of total energy in the exit channel between electron and photon, b) deep recoil regime where the bandwidth/energy spread of the two interacting beams are exchanged in the exit channel, 2) electron is stopped, i.e. taken down at rest in the laboratory system by colliding with an incident photon of $mc^2/2$ energy, c) electron back-scattering region, where incident electron is back-scattered by the incident photon, 3) symmetric scattering, when the incident particles carry equal and opposite momenta, so that in the exit channel they are back-scattered with same energy/momenta, d) Compton scattering ($a'$ $la$ Arthur Compton, see ref.4), where photons carry an energy much larger than the colliding electron energy. For each region and/or transition point we discuss the potential effects of interest in diverse areas, like generating mono-chromatic gamma ray beams in deep recoil regions with spectral purification, or possible mechanisms of generation and propagation of very high energy photons in the cosmological domain.

Generative deep learning (DL) models have been successfully adopted for vulnerability patching. However, such models require the availability of a large dataset of patches to learn from. To overcome this issue, researchers have proposed to start from models pre-trained with general knowledge, either on the programming language or on similar tasks such as bug fixing. Despite the efforts in the area of automated vulnerability patching, there is a lack of systematic studies on how these different training procedures impact the performance of DL models for such a task. This paper provides a manyfold contribution to bridge this gap, by (i) comparing existing solutions of self-supervised and supervised pre-training for vulnerability patching; and (ii) for the first time, experimenting with different kinds of prompt-tuning for this task. The study required to train/test 23 DL models. We found that a supervised pre-training focused on bug-fixing, while expensive in terms of data collection, substantially improves DL-based vulnerability patching. When applying prompt-tuning on top of this supervised pre-trained model, there is no significant gain in performance. Instead, prompt-tuning is an effective and cheap solution to substantially boost the performance of self-supervised pre-trained models, i.e., those not relying on the bug-fixing pre-training.

Understanding the limits of language is a prerequisite for Large Language Models (LLMs) to act as theories of natural language. LLM performance in some language tasks presents both quantitative and qualitative differences from that of humans, however it remains to be determined whether such differences are amenable to model size. This work investigates the critical role of model scaling, determining whether increases in size make up for such differences between humans and models. We test three LLMs from different families (Bard, 137 billion parameters; ChatGPT-3.5, 175 billion; ChatGPT-4, 1.5 trillion) on a grammaticality judgment task featuring anaphora, center embedding, comparatives, and negative polarity. N=1,200 judgments are collected and scored for accuracy, stability, and improvements in accuracy upon repeated presentation of a prompt. Results of the best performing LLM, ChatGPT-4, are compared to results of n=80 humans on the same stimuli. We find that humans are overall less accurate than ChatGPT-4 (76% vs. 80% accuracy, respectively), but that this is due to ChatGPT-4 outperforming humans only in one task condition, namely on grammatical sentences. Additionally, ChatGPT-4 wavers more than humans in its answers (12.5% vs. 9.6% likelihood of an oscillating answer, respectively). Thus, while increased model size may lead to better performance, LLMs are still not sensitive to (un)grammaticality the same way as humans are. It seems possible but unlikely that scaling alone can fix this issue. We interpret these results by comparing language learning in vivo and in silico, identifying three critical differences concerning (i) the type of evidence, (ii) the poverty of the stimulus, and (iii) the occurrence of semantic hallucinations due to impenetrable linguistic reference.

Although the mass growth of supermassive black holes during the Epoch of Reionisation is expected to play a role in shaping the concurrent growth of their host-galaxies, observational evidence of feedback at z$\gtrsim$6 is still sparse. We perform the first multi-scale and multi-phase characterisation of black-hole driven outflows in the $z\sim6.6$ quasar J0923+0402 and assess how these winds impact the cold gas reservoir. We employ the SimBAL spectral synthesis to fit broad absorption line (BAL) features and find a powerful ionized outflow on $\lesssim210$ pc scale, with a kinetic power $\sim2-100$\% of the quasar luminosity. ALMA observations of [CII] emission allow us to study the morphology and kinematics of the cold gas. We detect high-velocity [CII] emission, likely associated with a cold neutral outflow at $\sim0.5-2$ kpc scale in the host-galaxy, and a bright extended [CII] halo with a size of $\sim15$ kpc. For the first time at such an early epoch, we accurately constrain the outflow energetics in both the ionized and the atomic neutral gas phases. We find such energetics to be consistent with expectations for an efficient feedback mechanism, and both ejective and preventative feedback modes are likely at play. The scales and energetics of the ionized and atomic outflows suggest that they might be associated with different quasar accretion episodes. The results of this work indicate that strong black hole feedback is occurring in quasars at $z\gtrsim6$ and is likely responsible for shaping the properties of the cold gas reservoir up to circum-galactic scales., Comment: Accepted for publication in ApJ

Redundancy in biology may be explained by the need to optimize extreme searching processes, where one or few among many particles are requested to reach the target like in human fertilization. We show that non-Gaussian rare fluctuations in Brownian diffusion dominates such searches, introducing drastic corrections to the known Gaussian behavior. Our demonstration entails different physical systems and pinpoints the relevance of diversity within redundancy to boost fast targeting. We sketch an experimental context to test our results: polydisperse systems.

Diffusing diffusivity models, polymers in the grand canonical ensemble and polydisperse, and continuous-time random walks all exhibit stages of non-Gaussian diffusion. Is non-Gaussian targeting more efficient than Gaussian? We address this question, central to, e.g., diffusion-limited reactions and some biological processes, through a general approach that makes use of Jensen's inequality and that encompasses all these systems. In terms of customary mean first-passage time, we show that Gaussian searches are more effective than non-Gaussian ones. A companion paper argues that non-Gaussianity becomes instead highly more efficient in applications where only a small fraction of tracers is required to reach the target.

Let $R_n=K[x_1,\dots,x_n]$ be the $n$-variable polynomial ring over a field $K$. Let $S_n$ denote the set of monomials in $R_n$. A monomial $u \in S_n$ is a \textit{Gotzmann monomial} if the Borel-stable monomial ideal $\langle u \rangle$ it generates in $R_n$ is a Gotzmann ideal. A longstanding open problem is to determine all Gotzmann monomials in $R_n$. Given $u_0 \in S_{n-1}$, its \textit{Gotzmann threshold} is the unique nonnegative integer $t_0=\tau_n(u_0)$ such that $u_0x_n^t$ is a Gotzmann monomial in $R_n$ if and only if $t \ge t_0$. Currently, the function $\tau_n$ is exactly known for $n \le 4$ only. We present here an efficient procedure to determine $\tau_n(u_0)$ for all $n$ and all $u_0 \in S_{n-1}$. As an application, in the critical case $u_0=x_2^d$, we determine $\tau_5(x_2^d)$ for all $d$ and we conjecture that for $n \ge 6$, $\tau_n(x_2^d)$ is a polynomial in $d$ of degree $2^{n-2}$ and dominant term equal to that of the $(n-2)$-iterated binomial coefficient $$ \binom {\binom {\binom d2}2}{\stackrel{\cdots}2}. $$, Comment: 28 pages

Human behaviour strongly influences the spread of infectious diseases: understanding the interplay between epidemic dynamics and adaptive behaviours is essential to improve response strategies to epidemics, with the goal of containing the epidemic while preserving a sufficient level of operativeness in the population. Through activity-driven temporal networks, we formulate a general framework which models a wide range of adaptive behaviours and mitigation strategies, observed in real populations. We analytically derive the conditions for a widespread diffusion of epidemics in the presence of arbitrary adaptive behaviours, highlighting the crucial role of correlations between agents behaviour in the infected and in the susceptible state. We focus on the effects of sick-leave, comparing the effectiveness of different strategies in reducing the impact of the epidemic and preserving the system operativeness. We show the critical relevance of heterogeneity in individual behavior: in homogeneous networks, all sick-leave strategies are equivalent and poorly effective, while in heterogeneous networks, strategies targeting the most vulnerable nodes are able to effectively mitigate the epidemic, also avoiding a deterioration in system activity and maintaining a low level of absenteeism. Interestingly, with targeted strategies both the minimum of population activity and the maximum of absenteeism anticipate the infection peak, which is effectively flattened and delayed, so that full operativeness is almost restored when the infection peak arrives. We also provide realistic estimates of the model parameters for influenza-like illness, thereby suggesting strategies for managing epidemics and absenteeism in realistic populations., Comment: 16 pages, 8 figures

Magnetic nanoparticles (MNPs) are the foundation of several new strategies for neural repair and neurological therapies. The fact that a remote force can act on MNPs at the cytoplasmic space constitutes the essence of many new neurotherapeutic concepts. MNPs with a predesigned physicochemical characteristic can interact with external magnetic fields to apply mechanical forces in definite areas of the cell to modulate cellular behaviour. Magnetic actuation to direct the outgrowth on neurons after nerve injury has already demonstrated the therapeutic potential for neural repair. When these magnetic cores are functionalized with molecules such as nerve growth factors or neuroprotective molecules, multifunctional devices can be developed. This chapter will review some of these new nanotechnology-based solutions for neurological diseases, specifically those based on the use of engineered MNPs used for neuroprotection and neuroregeneration. These include the use of MNPs as magnetic actuators to guide neural cells, modulate intracellular transport and stimulate axonal growth after nerve injury., Comment: 35 pages; 6 figures; Chapter 22 of Magnetic Nanoparticles: From Fabrication to Clinical Applications

Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however it is often unrealistic. Efforts in modeling non-exponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (infectious period distribution) and the modeling needs (corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. We show that the same infectious period distribution at the population level can be reproduced by two modeling schemes -- host-based and population-based -- depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of an SIS transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on infection duration. In absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.