Search

Extended objects (defects) in Quantum Field Theory exhibit rich, nontrivial dynamics describing a variety of physical phenomena. These systems often involve strong coupling at long distances, where the bulk and defects interact, making analytical studies challenging. By carefully analyzing the behavior of bulk symmetries in the presence of defects, we uncover robust topological constraints on defect RG flows. Specifically, we introduce the notions of $\textit{defect anomalies}$ and $\textit{strongly symmetric defects}$, both of which are RG-invariant. Several known notions, such as higher-form symmetries, fractionalization, and projective lines, are revealed to be manifestations of defect anomalies, which also encompass novel phenomena and forbid trivial defect dynamics in the IR. Meanwhile, strongly symmetric defects are shown to remain coupled at low energies, imposing powerful dynamical constraints. We verify our findings through concrete examples: exactly solvable defect RG flows in (1+1)d Conformal Field Theories with strongly symmetric lines and a surface defect in (2+1)d scalar QED., Comment: 31 pages, 3 figures, comments are welcome

Flux tuning of qubit frequencies in superconducting quantum processors is fundamental for implementing single and multi-qubit gates in quantum algorithms. Typical architectures involve the use of DC or fast RF lines. However, these lines introduce significant heat dissipation and undesirable decoherence mechanisms, leading to a severe bottleneck for scalability. Among different solutions to overcome this issue, we propose integrating tunnel Superconductor-Insulating-thin superconducting interlayer-Ferromagnet-Superconductor Josephson junctions (SIsFS JJs) into a novel transmon qubit design, the so-called ferrotransmon. SIsFS JJs provide memory properties due to the presence of ferromagnetic barriers and preserve at the same time the low-dissipative behavior of tunnel-insulating JJs, thus promoting an alternative tuning of the qubit frequency. In this work, we discuss the fundamental steps towards the implementation of this hybrid ferromagnetic transmon. We will give a special focus on the design, simulations, and preliminary experimental characterization of superconducting lines to provide in-plane magnetic fields, fundamental for an on-chip control of the qubit frequencies in the ferrotransmon.

Magnetic Josephson Junctions (MJJs) are a special class of hybrid systems where antagonistic correlations coexist, thus providing a key for advances in weak superconductivity, superconducting spintronics and quantum computation. So far, the memory properties of MJJs have been mostly investigated in view of digital electronics and for spintronic devices at liquid-helium temperature. At the operating temperature of quantum circuits, a magnetic order can rise in a Superconductor (S) at the S/Ferromagnet (F) interface, i.e., the inverse proximity effect (IPE), thus leading to a significant modification of the magnetic field patterns in MJJs. In this work, we have carried out a comparative investigation of the magnetic behavior of tunnel MJJs with a strong ferromagnetic layer inserted in the layout of both Nb and Al JJs, respectively. The comparative analysis validates the crucial role of the temperature, the fundamental scaling energies of S/F coupling systems, and the transparency of the S/F interface. This investigation points out that the IPE is a key aspect to consider when designing tunnel MJJs operating well below 4 K and thus in the perspective of hybrid superconducting quantum architectures., Comment: 14 pages, 4 figures, published in Low Temperature Physics

We investigate the advantages of high-dimensional encoding for a quantum key distribution protocol. In particular, we address a BBM92-like protocol where the dimension of the systems can be larger than two and more than two mutually unbiased bases (MUBs) can be employed. Indeed, it is known that, for a system whose dimension $d$ is a prime or the power of a prime, up to $d+1$ MUBs can be found. We derive an analytic expression for the asymptotic key rate when $d+1$ MUBs are exploited and show the effects of using different numbers of MUBs on the performance of the protocol. Then, we move to the non-asymptotic case and optimize the finite key rate against collective and coherent attacks for generic dimension of the systems and all possible numbers of MUBs. In the finite-key scenario, we find that, if the number of rounds is small enough, the highest key rate is obtained by exploiting three MUBs, instead of $d+1$ as one may expect., Comment: 11 pages, 6 figures

Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural architectures. At the same time, their simplicity makes them easy to use, amenable to theoretical analysis, yielding interpretable models in applications. Here, we focus on reviewing the role that the activation functions, describing the input-output relationship of single neurons in RBM, play in the functionality of these models. We discuss recent theoretical results on the benefits and limitations of different activation functions. We also review applications to biological data analysis, namely neural data analysis, where RBM units are mostly taken to have sigmoid activation functions and binary units, to protein data analysis and immunology where non-binary units and non-sigmoid activation functions have recently been shown to yield important insights into the data. Finally, we discuss open problems addressing which can shed light on broader issues in neural network research., Comment: 7 pages, 3 figures. To be published in EPL as di Sarra et al 2025 EPL. Accepted manuscript available online at https://doi.org/10.1209/0295-5075/ada636

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, which we validate using data from Italy starting in September 2020. SEIHRDV features the following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D) and Vaccinated (V). The model is age-stratified, as it considers the population split into 15 age groups. Moreover, it takes into account 7 different contexts of exposition to the infection (family, home, school, work, transport, leisure, other contexts), which impact on the transmission mechanism. Thanks to these features, the model can address the analysis of the epidemics and the efficacy of non-pharmaceutical interventions, as well as possible vaccination strategies and the introduction of the Green Pass, a containment measure introduced in Italy in 2021. By leveraging on the SEIHRDV model, we successfully analyzed epidemic trends during the COVID-19 outbreak from September 2020 to July 2021. The model proved instrumental in conducting comprehensive what-if studies and scenario analyses tailored to Italy and its regions. Furthermore, SEIHRDV facilitated accurate forecasting of the future potential trajectory of the epidemic, providing critical information for informed decision making and public health strategies., Comment: 26 pages, 25 figures, 3 tables

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings., Comment: Submitted to RA-L

This paper extends the sample complexity theory for ill-posed inverse problems developed in a recent work by the authors [`Compressed sensing for inverse problems and the sample complexity of the sparse Radon transform', J. Eur. Math. Soc., to appear], which was originally focused on the sparse Radon transform. We demonstrate that the underlying abstract framework, based on infinite-dimensional compressed sensing and generalized sampling techniques, can effectively handle a variety of practical applications. Specifically, we analyze three case studies: (1) The reconstruction of a sparse signal from a finite number of pointwise blurred samples; (2) The recovery of the (sparse) source term of an elliptic partial differential equation from finite samples of the solution; and (3) A moderately ill-posed variation of the classical sensing problem of recovering a wavelet-sparse signal from finite Fourier samples, motivated by magnetic resonance imaging. For each application, we establish rigorous recovery guarantees by verifying the key theoretical requirements, including quasi-diagonalization and coherence bounds. Our analysis reveals that careful consideration of balancing properties and optimized sampling strategies can lead to improved reconstruction performance. The results provide a unified theoretical foundation for compressed sensing approaches to inverse problems while yielding practical insights for specific applications., Comment: 47 pages

We introduce the novel task of Crowd Volume Estimation (CVE), defined as the process of estimating the collective body volume of crowds using only RGB images. Besides event management and public safety, CVE can be instrumental in approximating body weight, unlocking weight sensitive applications such as infrastructure stress assessment, and assuring even weight balance. We propose the first benchmark for CVE, comprising ANTHROPOS-V, a synthetic photorealistic video dataset featuring crowds in diverse urban environments. Its annotations include each person's volume, SMPL shape parameters, and keypoints. Also, we explore metrics pertinent to CVE, define baseline models adapted from Human Mesh Recovery and Crowd Counting domains, and propose a CVE specific methodology that surpasses baselines. Although synthetic, the weights and heights of individuals are aligned with the real-world population distribution across genders, and they transfer to the downstream task of CVE from real images. Benchmark and code are available at github.com/colloroneluca/Crowd-Volume-Estimation.

Recent discoveries from time-domain surveys are defying our expectations for how matter accretes onto supermassive black holes (SMBHs). The increased rate of short-timescale, repetitive events around SMBHs, including the newly-discovered quasi-periodic eruptions (QPEs), are garnering further interest in stellar-mass companions around SMBHs and the progenitors to mHz frequency gravitational wave events. Here we report the discovery of a highly significant mHz Quasi-Periodic Oscillation (QPO) in an actively accreting SMBH, 1ES 1927+654, which underwent a major optical, UV, and X-ray outburst beginning in 2018. The QPO was first detected in 2022 with a roughly 18-minute period, corresponding to coherent motion on scales of less than 10 gravitational radii, much closer to the SMBH than typical QPEs. The period decreased to 7.1 minutes over two years with a decelerating period evolution ($\ddot{P} > 0$). This evolution has never been seen in SMBH QPOs or high-frequency QPOs in stellar mass black holes. Models invoking orbital decay of a stellar-mass companion struggle to explain the period evolution without stable mass transfer to offset angular momentum losses, while the lack of a direct analog to stellar mass black hole QPOs means that many instability models cannot explain all of the observed properties of the QPO in 1ES 1927+654. Future X-ray monitoring will test these models, and if it is a stellar-mass orbiter, the Laser Interferometer Space Antenna (LISA) should detect its low-frequency gravitational wave emission., Comment: Accepted in Nature

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

Traditional linear approximation methods, such as proper orthogonal decomposition and the reduced basis method, are ineffective for transport-dominated problems due to the slow decay of the Kolmogorov $n$-width. This results in reduced-order models that are both inefficient and inaccurate. In this work, we present an approach for the model reduction of transport-dominated problems by employing cross-correlation based snapshot registration, accelerating the Kolmogorov $n$-width decay, and enabling the construction of efficient reduced-order models using linear methods. We propose a complete framework comprising offline-online stages for the development of reduced-order models using the cross-correlation based snapshots registration. The effectiveness of the proposed approach is demonstrated using two test cases: 1D travelling waves and the higher-order methods benchmark test case, 2D isentropic convective vortex.

The study illustrates a first step towards an ongoing work aimed at developing a dataset of dialogues potentially useful for customer service conversation management between humans and AI chatbots. The approach exploits ChatGPT 3.5 to generate dialogues. One of the requirements is that the dialogue is characterized by a specific language proficiency level of the user; the other one is that the user expresses a specific emotion during the interaction. The generated dialogues were then evaluated for overall quality. The complexity of the language used by both humans and AI agents, has been evaluated by using standard complexity measurements. Furthermore, the attitudes and interaction patterns exhibited by the chatbot at each turn have been stored for further detection of common conversation patterns in specific emotional contexts. The methodology could improve human-AI dialogue effectiveness and serve as a basis for systems that can learn from user interactions.

3C 216 is an extra-galactic radio source classified as a compact steep spectrum (CSS) object, associated with the source 4FGL J0910.0+4257 detected by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope. The source exhibits extended radio structures as well as an inner relativistic jet. In general, jets accelerated by active galactic nuclei (AGNs) are efficient sources of non-thermal radiation, spanning from the radio band to X-ray and gamma-ray energies. Due to relativistic beaming, much of this radiation, particularly in the high-energy domain, is concentrated within a narrow cone aligned with the jet's direction. Consequently, high-energy emission is more easily detected in blazars, where the jet is closely aligned with the line of sight of the observer. Beginning in November 2022, Fermi-LAT observed increased gamma-ray activity from 3C 216, culminating in a strong outburst in May 2023. This event was followed up by observations from the Neil Gehrels Swift Observatory telescope. In this work, we perform a careful analysis of the multifrequency data (gamma-ray, X-ray, UV, optical) collected during this observational campaign. We find that the spectral energy distribution of the flaring source evolves in a coherent way, suggesting a common origin for the multifrequency emission. These results support the interpretation of the gamma-ray emission within a single zone synchrotron self-Compton (SSC) model, with important implications for the mechanisms powering high-energy radiation in AGN jets., Comment: submitted to ApJ

Recent simulations of wave dark matter around black hole binaries revealed the formation of a universal density profile that co-rotates with the binary. We derive this profile from first principles, interpreting it as the steady state of a scattering process. We find that the scattering becomes particularly efficient when the ratio of the binary separation to the dark matter's de Broglie wavelength assumes certain discrete values, which can be interpreted as bound state resonances. After estimating the amount of dark matter that undergoes this type of scattering off supermassive black hole binaries at galactic centers, we demonstrate that the process can induce an observable modification of the slope of the Pulsar Timing Array spectrum. This opens up a new possibility to gain insights on the nature of dark matter from observations of low-frequency gravitational waves., Comment: 8 pages, 4 figures. Corrected normalization and units of $P/\dot M$

We theoretically investigate magnon heat transport in an antiferromagnetic (AFM) insulator containing a domain wall (DW) in the presence of a magnetic field applied along the easy axis. We show that the intrinsic spin of the DW couples to the external magnetic field which modifies the transmission of spin wave (SW) through the DW. Applying the magnetic field lifts the degeneracy between two AFM magnon modes and results in different occupation numbers for the two magnon modes. Combined with the finite reflection of a narrow domain wall, this is found to have a significant impact on the magnon heat transport, giving rise to thermal magnetoresistance. Our findings suggest that an AFM DW can be used as a controllable element for regulating the magnon heat current in magnonic devices through the application of a magnetic field.

Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic picture of the offensive potential of AI. In this SoK paper we seek to lay the ground for a systematic analysis of the heterogeneous capabilities of offensive AI. In particular we (i) account for AI risks to both humans and systems while (ii) consolidating and distilling knowledge from academic literature, expert opinions, industrial venues, as well as laypeople -- all of which being valuable sources of information on offensive AI. To enable alignment of such diverse sources of knowledge, we devise a common set of criteria reflecting essential technological factors related to offensive AI. With the help of such criteria, we systematically analyze: 95 research papers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user study (N=549) entailing individuals with diverse backgrounds and expertise; and the opinion of 12 experts. Our contributions not only reveal concerning ways (some of which overlooked by prior work) in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come., Comment: Systemization of Knowledge (SoK) paper. Accepted to the 3rd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML'25)

We study the evolution of the fastest paths in transportation networks under increasing congestion, modeled as a linear decrease in edge travel speed with density due to interactions among selfish agents. Moving from the common edge-based to a path-based analysis, we examine the fastest directed routes connecting random origin-destination pairs as traffic grows, characterizing their shape through effective length, maximum detour, and area under the curve, and their performance through a novel metric measuring how fast and how far an agent travels toward its destination. The entire network is characterized by analyzing the performance metric's distribution across uniformly distributed paths. The study covers both random planar networks with controlled characteristics and real urban networks of major cities. The low-density network regime, in which an initial smooth performance degradation is observed up to a critical traffic volume, is followed by the emergence of complex patterns of spatially heterogeneous slowdowns as traffic increases, rapidly leading to disjoint subnetworks. The failure of a few edges leads to a catastrophic decrease in the network performance. The fastest paths for all cities show a peak for detour and inness (and their variance) in the proximity of the critical traffic level, defined as the flex of the rejected path ratio curve. Inness generally shows a slight attraction by city centers on paths for light traffic, but this reverses to strong repulsion during congestion. We exploit path performance to uncover an asymmetric behavior of different regions of the networks when acting as origins or destinations. Finally, the Gini coefficient is used to study the unequal effects of path performance degradation with traffic., Comment: Submitted to Phys. Rev. E, 12 pages, one Supplemental Material PDF file

High-dimensional encoding for quantum key distribution (QKD) enables increased achievable key rates and robustness as compared to the standard qubit-based systems. However, experimental implementations of such systems are more complicated, expensive, and require complex security analysis. In this work we present a proof of principle high-dimensional time-phase BB84 QKD experiment using only one single-photon detector per measurement basis. We employ the temporal Talbot effect to detect QKD symbols in the control basis, and show experimentally-obtained values for the two-dimensional and four-dimensional case. We present a comparison of the secret key rates obtained from two different security proofs to display security issues stemming from asymmetric detection efficiencies in the two bases. Our results contribute to the discussion of the benefits of high-dimensional encoding and highlight the impact of security analysis on the achievable QKD performance., Comment: 8 pages, 8 figures

Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improving the system's lifetime. Most existing estimation methods are either physics-based digital twins of the battery or data-driven models such as Neural Networks (NNs). In this work, we propose two new contributions in this domain. First, we introduce a novel NN architecture formed by two cascaded branches: one to predict the current SoC based on sensor readings, and one to estimate the SoC at a future time as a function of the load behavior. Second, we integrate battery dynamics equations into the training of our NN, merging the physics-based and data-driven approaches, to improve the models' generalization over variable prediction horizons. We validate our approach on two publicly accessible datasets, showing that our Physics-Informed Neural Networks (PINNs) outperform purely data-driven ones while also obtaining superior prediction accuracy with a smaller architecture with respect to the state-of-the-art.

This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an optimal synthesis operator, denoted as $B$, which regularizes the inverse problem while promoting sparsity in the solution. The method leverages statistical properties of the underlying data and incorporates prior knowledge through the choice of $B$. We establish the well-posedness of the optimization problem, provide theoretical guarantees for the learning process, and present sample complexity bounds. The approach is demonstrated through examples, including compact perturbations of a known operator and the problem of learning the mother wavelet, showcasing its flexibility in incorporating prior knowledge into the regularization framework. This work extends previous efforts in Tikhonov regularization by addressing non-differentiable norms and proposing a data-driven approach for sparse regularization in infinite dimensions.

This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is based on spatial relationships in environments where direct communication between agents is not allowed, leveraging the ubiquity of spatial reasoning in real-world multi-agent scenarios. We introduce MARC (Multi-Agent Relational Critic), a simple yet effective critic architecture incorporating spatial relational inductive biases by transforming the state into a spatial graph and processing it through a relational graph neural network. The performance of MARC is evaluated across six collaborative tasks, including a novel environment with heterogeneous agents. We conduct a comprehensive empirical analysis, comparing MARC against state-of-the-art MARL baselines, demonstrating improvements in both sample efficiency and asymptotic performance, as well as its potential for generalization. Our findings suggest that a minimal integration of spatial relational inductive biases as abstraction can yield substantial benefits without requiring complex designs or task-specific engineering. This work provides insights into the potential of relational state abstraction to address sample efficiency, a key challenge in MARL, offering a promising direction for developing more efficient algorithms in spatially complex environments.

We present JWST/NIRSpec IFU observations of the z=7.152 galaxy system B14-65666, as part of the GA-NIFS survey. Line and continuum emission in this massive system (log10(M*/Msol)=9.8+/-0.2) is resolved into two strong cores, two weaker clumps, and a faint arc, as seen in recent JWST/NIRCam imaging. Our dataset contains detections of [OII]3727,3729, [NeIII]3869,3968, Balmer lines (HBeta, HGamma, HDelta, HEpsilon, HZeta), [OIII]5007, and weak [OIII]4363. Each spectrum is fit with a model that consistently incorporates interstellar medium conditions (i.e., electron temperature, T_e, electron density, n_e, and colour excess, E(B-V)). The resulting line fluxes are used to constrain the gas-phase metallicity (~0.3-0.4 solar) and HBeta-based SFR (310+/-40 Msol/yr) for each region. Common line ratio diagrams (O32-R23, R3-R2, Ne3O2-R23) reveal that each line-emitting region lies at the intersection of local and high-redshift galaxies, suggesting low ionisation and higher metallicity compared to the predominantly lower-mass galaxies studied with the JWST/NIRSpec IFU so far at z>5.5. Spaxel-by-spaxel fits reveal evidence for both narrow (FWHM<400 km/s) and broad (FWHM >500 km/s) line emission, the latter of which likely represents tidal interaction or outflows. Comparison to ALMA [CII]158um and [OIII]88um data shows a similar velocity structure, and optical-far infrared diagnostics suggest regions of high Lyman continuum escape fraction and n_e. This source lies on the mass-metallicity relation at z>4, suggesting an evolved nature. The two core galaxies show contrasting properties (e.g., SFR, M*, gas-phase metallicity), suggesting distinct evolutionary pathways. Combining the NIRSpec IFU and ALMA data sets, our analysis opens new windows into the merging system B14-65666., Comment: 23 pages, 15 figures. Submitted to MNRAS

Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents. Moreover, the Manifold Clustering Hypothesis suggests that despite these high-dimensional representations, documents relevant to a query reside on a lower-dimensional, query-dependent manifold. While this hypothesis has inspired new retrieval methods, existing approaches still face challenges in effectively separating non-relevant information from relevant signals. We propose a novel methodology that addresses these limitations by leveraging information from both relevant and non-relevant documents. Our method, ECLIPSE, computes a centroid based on irrelevant documents as a reference to estimate noisy dimensions present in relevant ones, enhancing retrieval performance. Extensive experiments on three in-domain and one out-of-domain benchmarks demonstrate an average improvement of up to 19.50% (resp. 22.35%) in mAP(AP) and 11.42% (resp. 13.10%) in nDCG@10 w.r.t. the DIME-based baseline (resp. the baseline using all dimensions). Our results pave the way for more robust, pseudo-irrelevance-based retrieval systems in future IR research.

We propose a revised formulation of General Relativity for cosmological contexts, in which the Einstein constant varies with the energy density of the Universe. We demonstrate that this modification has no direct phenomenological impact on the Universe's dynamics or on particle motion within the expanding cosmos. Assuming a state close to vacuum, here defined by the vanishing product of the Einstein coupling constant and the Universe's energy density, we perform a Taylor expansion of the theory. In this framework, the vacuum energy problem is addressed, and an additional constant pressure term, which induces a Chaplygin-like contribution to the dark energy equation of state, arises in the late-time dynamics. The correction to the late-time Hubble parameter is investigated by comparing theoretical predictions with the late Universe observational data. Our findings indicate that the current value of the vacuum energy is consistent with zero. Alternatively, the expansion used in our formulation would no longer be valid if the current state significantly deviates from the assumed near-vacuum condition. Implications of the modified $\Lambda$CDM model with respect to the Hubble tension are also discussed., Comment: 17 pages, 3 figures

In this paper we construct a parametrix for the fractional Helmholtz equation $((-\Delta)^s - \tau^{2s} r(x)^{2s} + q(x))u=0$ making use of geometrical optics solutions. We show that the associated eikonal equation is the same as in the classical case, while in the first transport equation the effect of nonlocality is only visible in the zero-th order term, which depends on $s$. Moreover, we show that the approximate geometrical optics solutions present different behaviors in the regimes $s\in(0,\frac 12)$ and $s\in [\frac 12,1)$. While the latter case is quite similar to the classical one, which corresponds to $s=1$, in the former case we find that the potential is a strong perturbation, which changes the propagation of singularities. As an application, we study the inverse problem consisting in recovering the potential $q$ from Cauchy data when the refraction index $r$ is fixed and simple. Using our parametrix based on the construction of approximate geometrical optics solutions, we prove that H\"older stability holds for this problem. This is a substantial improvement over the state of the art for fractional wave equations, for which the usual Runge approximation argument can provide only logarithmic stability. Besides its mathematical novelty, this study is motivated by envisioned applications in nonlocal elasticity models emerging from the geophysical sciences., Comment: 31 pages, 0 figures

Merger events are thought to be an important phase in the assembly of massive galaxies. At the same time, Active Galactic Nuclei (AGN) play a fundamental role in the evolution of their star formation histories. Both phenomena can be observed at work in NGC 6240, a local prototypical merger, classified as an UltraLuminous InfraRed Galaxy (ULIRG) thanks to its elevated infrared luminosity. Interestingly, NGC 6240 hosts two AGN separated by 1.5''(~ 735 pc), detected in both X-ray and radio band. Taking advantage of the unprecedented sensitivity and wavelength coverage provided by the Integral Field Unit (IFU) of the NIRSpec instrument onboard JWST, we observed the nuclear region of NGC 6240 in a FoV of 3.7'' x 3.7''(1.9 x 1.9 kpc^2), to investigate gas kinematics and InterStellar Medium (ISM) properties with a high spatial resolution of ~ 0.1'' (or ~ 50 pc). We separated the different gas kinematic components through multi-Gaussian fitting and studied the excitation properties of the ISM from the NIR diagnostic diagram based on the H_2 1-0 S(1)/BrGamma and [Fe II]1.257micron/PaBeta lines ratios. We isolated the ionization cones of the two nuclei, and detected coronal lines emission from both of them. Using H_2 line ratios, we found that the molecular hydrogen gas is excited mostly by thermal processes. We computed a hot molecular gas mass of 1.3 x 10^5 M_sun and an ionized gas mass in the range of 10^5 - 10^7 M_sun. We studied with unprecedented spatial resolution and sensitivity the kinematics of the molecular and ionized gas phases. We revealed the complex structure of the molecular gas and found a blueshifted outflow near the Southern nucleus, together with filaments connecting a highly redshifted H_2 cloud with the two nuclei. We speculate on the possible nature of this H_2 cloud and propose two possible scenarios: either outflowing gas, or a tidal cloud falling onto the nuclei., Comment: 27 pages, 25 figures. Submitted to Astronomy & Astrophysics (A&A)

We study the behaviour of a Brownian particle in the overdamped regime in the presence of a harmonic potential, assuming its diffusion coefficient to randomly jump between two distinct values. In particular, we characterize the probability distribution of the particle position and provide detailed expressions for the mean square displacement and the kurtosis. We highlight non-Gaussian behaviour even within the long-term limit carried over with an excess of probability both in the central part and in the distribution's tails. Moreover, when one of the two diffusion coefficients assumes the value zero, we provide evidence that the probability distribution develops a cusp. Most of our results are analytical, and corroborated by numerical simulations., Comment: 9 pagine, 4 figure

In general, the comprehension of any type of complex system depends on the resolution used to look at the phenomena occurring within it. But identifying a priori, for example, the best time frequencies/scales to study a certain system over-time, or the spatial distances at which looking for correlations, symmetries, and fluctuations, is most often non-trivial. Here we describe an unsupervised approach that, starting solely from the data of a system, allows learning the characteristic length-scales of the key events/processes dominating it and the optimal spatiotemporal resolutions to characterize them. We test this approach on time-series data obtained from simulation or experimental trajectories of various example many-body complex systems ranging from the atomic- to the macroscopic-scale and having diverse internal dynamic complexities. Our method automatically performs the analysis of the system's data, analyzing correlations at all relevant inter-particle distances and at all possible inter-frame intervals in which their time-series can be subdivided: namely, at all space-and-time resolutions. The optimal spatiotemporal resolution for studying a certain system thus steps-out as that maximizing information extraction-and-classification from the system's data, which we prove being related to the characteristic spatiotemporal length-scales of the local/collective physical events dominating it. This approach is broadly applicable and can be used to optimize the study of different types of data (static distributions, time-series, or signals). The concept of 'optimal resolution' has general character and provides a robust basis to characterize any type of system based on its data, as well as to guide data analysis in general.

In this paper, we introduce a new family of argument-ranking semantics which can be seen as a refinement of the classification of arguments into skeptically accepted, credulously accepted and rejected. To this end we use so-called social ranking functions which have been developed recently to rank individuals based on their performance in groups. We provide necessary and sufficient conditions for a social ranking function to give rise to an argument-ranking semantics satisfying the desired refinement property.

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling assetuser matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This paper introduces a novel approach to address this gap by leveraging existing Bluetooth Low Energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: i) a wearable device, ii) an IoT device attached to or embedded in the assets, iii) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an Extended Kalman Filter (EKF), and iv) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets., Comment: This article has been accepted for publication in the IEEE Internet of Things Journal. DOI: https://doi.org/10.1109/JIOT.2024.3479458

In this paper we prove general criticality criteria for operators $\Delta + V$ on manifolds $M$ with two ends, where $V$ bounds the Ricci curvature of $M$, and a related spectral splitting theorem. We apply them to get new rigidity results for stable and $\delta$-stable minimal hypersurfaces in manifolds with non-negative bi-Ricci or sectional curvature, in ambient dimension up to $5$ and $6$, respectively. In the special case where the ambient space is $\mathbb{R}^4$, we prove that a $1/3$-stable minimal hypersurface must either have one end or be a catenoid. As a consequence, any proper, $\delta$-stable minimal hypersurface with $\delta > 1/3$ and finite fundamental group must be a hyperplane., Comment: 33pp, revised version. We have removed Corollary 3.10, since it can be proved by simpler means, and corrected the statement of Theorem 1.2 by adding the assumption that M has finite fundamental group. Minor further changes throughout the paper and more references added. Package axessibility included to make the paper available to visually impaired people

Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box policies that are effective, but suffer from an inherent opacity. Interpretable policies, while generally underperforming compared to their closed-box counterparts, advantageously facilitate transparent decision-making within automated systems. Hence, their usage is often essential for diagnosing and mitigating errors, supporting ethical and legal accountability, and fostering trust among stakeholders. In this paper, we propose SMOSE, a novel method to train sparsely activated interpretable controllers, based on a top-1 Mixture-of-Experts architecture. SMOSE combines a set of interpretable decisionmakers, trained to be experts in different basic skills, and an interpretable router that assigns tasks among the experts. The training is carried out via state-of-the-art Reinforcement Learning algorithms, exploiting load-balancing techniques to ensure fair expert usage. We then distill decision trees from the weights of the router, significantly improving the ease of interpretation. We evaluate SMOSE on six benchmark environments from MuJoCo: our method outperforms recent interpretable baselines and narrows the gap with noninterpretable state-of-the-art algorithms, Comment: To be published in the Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI-25)

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country's scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices., Comment: The main body of the paper has 16 pages, 11 figures and 4 tables. The supplementary material has 7 pages, 6 figures and no tables

Several models of physics beyond the Standard Model predict the existence of new ultralight bosons. This thesis investigates a way to discover such particles through observations of gravitational waves from binary black holes. This is possible through black hole superradiance, which spontaneously creates a "boson cloud" around a rapidly spinning black hole. The system is also known as a gravitational atom, due to its similarities with the hydrogen atom. The thesis focuses on a scenario where a gravitational atom is orbited by a binary companion. The goal is to characterize the dynamics of the system and identify the signatures left by the boson cloud on the gravitational waves emitted by the binary. The predictions can be tested with current and future interferometers, such as LISA, LIGO, DECIGO, Einstein Telescope and TianQin. First, I demonstrate that the cloud catalyzes the binary formation by increasing the dynamical capture cross section. I then introduce and study the ionization of the cloud, wherein the perturbation from the binary unbinds the bosons, analogous to the photoelectric effect in atomic physics. After that, I examine the accretion of the cloud on the companion black hole. To achieve realistic and complete results, I proceed to extend the treatment of ionization, as well as of the orbital resonances discussed in earlier works, to orbits with generic inclination and eccentricity. This allows to study the entire history of the system, from formation to merger. The most distinctive observational signatures of the cloud are found to be the orbital energy lost through ionization and the preference for specific inclinations and eccentricities induced by the orbital resonances., Comment: PhD thesis, University of Amsterdam, 2024, 186 pages

In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons and Social Dilemmas frequently manifest in our daily lives. As artificial agents increasingly serve as autonomous proxies for humans, we propose using multi-agent reinforcement learning (MARL) to address this issue - learning policies to maximise collective returns even when individual agents' interests conflict with the collective one. Traditional MARL solutions involve sharing rewards, values, and policies or designing intrinsic rewards to encourage agents to learn collectively optimal policies. We introduce a novel MARL approach based on Suggestion Sharing (SS), where agents exchange only action suggestions. This method enables effective cooperation without the need to design intrinsic rewards, achieving strong performance while revealing less private information compared to sharing rewards, values, or policies. Our theoretical analysis establishes a bound on the discrepancy between collective and individual objectives, demonstrating how sharing suggestions can align agents' behaviours with the collective objective. Experimental results demonstrate that SS performs competitively with baselines that rely on value or policy sharing or intrinsic rewards.

The broad-band optical spectroscopy was used to study the optical and the hidden transport properties of the Ba(Fe$_{1-x}$Ni$_x$)$_2$As$_2$ superconducting films with different Ni contents. The normal state data were analyzed using a Drude-Lorentz model with two Drude components: narrow and broad ones. In the superconducting state, two gaps with $2\Delta _{0}^{(1)}/k_{B}T_{c}=1.57$ and $2\Delta _{0}^{(2)}/k_{B}T_{c}=3.48$ are formed for the Ba(Fe$_{0.965}$Ni$_{0.035}$)$_2$As$_2$ films, while for the Ba(Fe$_{0.95}$Ni$_{0.05}$)$_2$As$_2$ films these characteristic ratios are 1.88--2.08 and 3.66--4.13. Both gaps are formed from the narrow Drude component, whereas the broad Drude component remains ungapped. The calculated from infrared data total dc resistivity of the films with Ni contents $x=0.05$ and $x=0.08$ as well as the low-temperature scattering rate for the narrow Drude component show a hidden Fermi-liquid behavior. On the contrary, the films with $x=0.035$ manifest a hidden non-Fermi-liquid behavior. The Allen theory generalized to a multiband systems was applied to the analysis of the temperature dependences of a resistivity of the Ba(Fe$_{1-x}$Ni$_x$)$_2$As$_2$ films. The change of total electron-boson coupling and representative energy in the normal state versus the superconducting state for this system was shown to be typical of other iron-based superconducting materials as well as high-temperature superconducting cuprates., Comment: 27 pages, 10 figures, 2 tables

Secondary cosmic ray fluxes are important probes of the propagation and interaction of high-energy particles in the Galaxy. Recent measurements of primary and secondary cosmic ray nuclei have revealed unexpected spectral features that demand a deeper understanding. In this work we report the direct measurement of the cosmic ray boron spectrum from 10 GeV/n to 8 TeV/n with eight years of data collected by the Dark Matter Particle Explorer (DAMPE) mission. The measured spectrum shows an evident hardening at $182\pm24$ GeV/n with a spectral power index of $\gamma_1 = 3.02 \pm 0.01$ before the break and an index change of $\Delta \gamma = 0.31 \pm 0.05$ after the break. A simple power law model is disfavored at a confidence level of 8$\sigma$. Compared with the hardenings measured in the DAMPE proton and helium spectra, the secondary boron spectrum hardens roughly twice as much as these primaries, which is consistent with a propagation related mechanism to interpret the spectral hardenings of cosmic rays observed at hundreds of GeV/n., Comment: 10 pages, 10 figures, submitted to PRL

We try to approach a known equivalence to RH involving relative maxima and minima of Xi(t) on critical line by a representation of the derivative of the phase of Xi(s) with respect to imaginary coordinate that involves directly Euler product. In this attempt it is found an object conjectured to be the ``spectrum'' of prime numbers and an interesting widening of zero-free region. Reasons and consequences of the conjecture are highlighted., Comment: 20 pages , 6 figures

We study the behavior of perturbations in a compressible one-dimensional inviscid gas with an ambient state consisting of constant pressure and periodically-varying density. We show through asymptotic analysis that long-wavelength perturbations approximately obey a system of dispersive nonlinear wave equations. Computational experiments demonstrate that solutions of the 1D Euler equations agree well with this dispersive model, with solutions consisting mainly of solitary waves. Shock formation seems to be avoided for moderate-amplitude initial data, while shock formation occurs for larger initial data. We investigate the threshold for transition between these behaviors, validating a previously-proposed criterion based on further computational experiments. These results support the existence of large-time non-breaking solutions to the 1D compressible Euler equations, as hypothesized in previous works.

Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to conventional ultrasound and potentially a better ability to differentiate clinically significant cancer from normal tissue. However, the features of prostate cancer remain subtle, with ambiguous borders with normal tissue and large variations in appearance, making it challenging for both machine learning and humans to localize it on micro-ultrasound images. We propose a novel Mask Enhanced Deeply-supervised Micro-US network, termed MedMusNet, to automatically and more accurately segment prostate cancer to be used as potential targets for biopsy procedures. MedMusNet leverages predicted masks of prostate cancer to enforce the learned features layer-wisely within the network, reducing the influence of noise and improving overall consistency across frames. MedMusNet successfully detected 76% of clinically significant cancer with a Dice Similarity Coefficient of 0.365, significantly outperforming the baseline Swin-M2F in specificity and accuracy (Wilcoxon test, Bonferroni correction, p-value<0.05). While the lesion-level and patient-level analyses showed improved performance compared to human experts and different baseline, the improvements did not reach statistical significance, likely on account of the small cohort. We have presented a novel approach to automatically detect and segment clinically significant prostate cancer on B-mode micro-ultrasound images. Our MedMusNet model outperformed other models, surpassing even human experts. These preliminary results suggest the potential for aiding urologists in prostate cancer diagnosis via biopsy and treatment decision-making.

We unify the variational hypocoercivity framework established by D. Albritton, S. Armstrong, J.-C. Mourrat, and M. Novack, with the notion of second-order lifts of reversible diffusion processes, recently introduced by A. Eberle and F. L\"orler. We give an abstract, yet fully constructive, presentation of the theory, so that it can be applied to a large class of linear kinetic equations. As this hypocoercivity technique does not twist the reference norm, we can recover accurate and sharp convergence rates in various models. Among those, adaptive Langevin dynamics (ALD) is discussed in full detail and we show that for near-quadratic potentials, with suitable choices of parameters, it is a near-optimal second-order lift of the overdamped Langevin dynamics. As a further consequence, we observe that the Generalised Langevin Equation (GLE) is a also a second-order lift, as the standard (kinetic) Langevin dynamics are, of the overdamped Langevin dynamics. Then, convergence of (GLE) cannot exceed ballistic speed, i.e.~the square root of the rate of the overdamped regime. We illustrate this phenomenon with explicit computations in a benchmark Gaussian case., Comment: 17 pages

Probe Requests are Wi-Fi management frames periodically sent by devices during network discovery. Tracking Probe Requests over time offers insights into movement patterns, traffic flows, and behavior trends, which are keys in applications such as urban planning, human mobility analysis, and retail analytics. To protect user privacy, techniques such as MAC address randomization are employed, periodically altering device MAC addresses to limit tracking. However, research has shown that these privacy measures can be circumvented. By analyzing the Information Elements (IE) within the Probe Request body, it is possible to fingerprint devices and track users over time. This paper presents a machine learning-based approach for fingerprinting Wi-Fi Probe Requests in a compact fashion. We utilize Asymmetric Pairwise Boosting to learn discriminating filters which are then used to process specific bit sequences in Probe Request frames, and quantize the results into a compact binary format. Extensive evaluation on public datasets demonstrates a two-order-of-magnitude storage reduction compared to existing methods while maintaining robust fingerprinting performance., Comment: Pre-print

We study the semi-inclusive hadroproduction of doubly bottomed tetraquarks ($X_{b\bar{b}q\bar{q}}$) as well as fully bottomed ones ($T_{4b}$), to which we collectively refer as "bottomonium-like" states. We rely upon the variable-flavor number-scheme fragmentation at leading power, where a single parton perturbatively splits into the corresponding Fock state, which then hadronizes into the color-neutral, observed tetraquark. To this end, we build new sets of DGLAP/HF-NRevo consistent, hadron-structure oriented collinear fragmentation functions, which we name TQHL1.1 and TQ4Q1.1 parametrizations. They extend and supersede the corresponding 1.0 versions recently derived in previous works. The first family describes the fragmentation of doubly heavy tetraquarks and is based on an improved version of the Suzuki model for the heavy-quark channel. The second family depicts the fragmentation of fully heavy tetraquarks and embodies initial-scale inputs for gluon and heavy-quark channels, both of them calculated by the hands of potential nonrelativistic QCD. As a phenomenological application, we provide novel predictions for tetraquark-plus-jet high-energy distributions, computed within the NLL/NLO$^+$ hybrid factorization from (sym)JETHAD, at 14 TeV and 100 TeV FCC., Comment: 48 pages, 17 figures, 430 references. Four novel sets of "TetraQuarks with Heavy and Light Flavors" (TQHL1.1) NLO collinear fragmentation functions and eight novel sets of "TetraQuarks with 4 heavy Quarks" (TQ4Q1.1) ones released in LHAPDF format and publicly available from https://github.com/FGCeliberto/Collinear_FFs/

The Schrieffer-Wolff transformation (SWT) is a foundational perturbative method for deriving effective Hamiltonians in quantum systems by systematically eliminating couplings between pairs of energy distant subspaces. Despite recent advancements, the implementation of SWTs for sufficiently complex systems remains computationally challenging and often requires extensive calculations that are prone to errors. In this work, we introduce an analytical software tool, SymPT (Symbolic Perturbation Theory), designed to automate the SWT and its extensions. Building on a universal framework developed in recent research, SymPT provides a systematic and generalizable solution for deriving the generator of the transformation, enabling accurate computation of effective Hamiltonians for arbitrary perturbative systems. The tool supports both time-independent and time-periodic Hamiltonians, extending beyond standard SWT to incorporate arbitrary coupling elimination, block-diagonalization and full-diagonalization routines, thus enabling precise handling of systems with intricate energy structures., Comment: 22 pages, 4 figures

This paper addresses a two-dimensional sharp interface variational model for solid-state dewetting of thin films with surface energies, introduced by Wang, Jiang, Bao, and Srolovitz in \cite{jiang2016solid}. Using the $H^{-1}$-gradient flow structure of the evolution law, short-time existence for a surface diffusion evolution equation with curvature regularization is established in the context of epitaxially strained two-dimensional films. The main novelty, as compared to the study of the wetting regime, is the presence of moving contact lines.

This work aims at improving our understanding of the conditions enabling the development of an Internal transport barriers (ITB), using a more comprehensive physical model, including low-$\beta$ electromagnetic flux-driven simulations. Our key findings are that electron dynamics is crucial for ITB formation even in an ITG scenario and that having $q_{\text{min}}$ close to a lowest order rational value (2 in our simulations) to allow for eddies self-interaction is a necessary ingredient. Electron dynamics has two critical effects. First, it leads to a structure formation characterized by strong zonal flows shearing rate, reduction of turbulence and profile corrugation. Second, it leads to zonal current sheets that result in a broadening of the minimum-q region, qualitatively consistent with the flux-tube simulations of Vol\v{c}okas et al. [1]. Flux-driven simulations performed with $q_{\text{min}}=2$ reveal the development of the transport barrier in the ion channel, forming at inner and outer radial positions with respect to the $q_{\text{min}}$ position. The ITB formation in flux-driven setup is not recovered if $q_{\text{min}} = 2.03$. Additionally, a simulation at higher $\rho^*$ indicates that the extent of the flattened region of the q-profile due to turbulent self-interaction does not change proportionally to $\rho^*$ or to $\rho_i$, but somewhere in between. On the other hand, the input power required to achieve similar on-axis temperatures appears to exhibit almost GyroBohm scaling (for the two considered $\rho^*$ values). Furthermore, considering an initial q-profile with $q_{\text{min}} = 2.01$, flux-driven simulations show that partial self-interaction can evolve to complete self-interaction. This occurs due to turbulent-driven zonal currents that lower and flatten the q-profile down to $q_{\text{min}} = 2.0$, in line with what is reported in Vol\v{c}okas et al.[1].

In this paper we consider semilinear wave equation and other second order $\sigma$-evolution equations with different (effective or non-effective) damping mechanisms driven by fractional Laplace operators; in particular, the nonlinear term is the product of a power nonlinearity $|u|^p$ with the critical exponent $p=p_{\mathrm{c}}(n)$ and a modulus of continuity $\mu(|u|)$. We derive a critical condition on the nonlinearity by proving a global in time existence result under the Dini condition on $\mu$ and a blow-up result when $\mu$ does not satisfy the Dini condition. Especially, in this latter case we determine new sharp estimates for the lifespan of local solutions, obtaining upper and lower bounds of the lifespan which coincide, except for some multiplicative constants. The proofs of the blow-up result and the upper bound estimates of the lifespan require an introduction of new test functions which allows to overcome some new difficulties due to the presence of both non-local differential operators and general nonlinearities.