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The reduced dielectric screening in atomically thin semiconductors leads to remarkably strong electron interactions. As a result, bound electron-hole pairs (excitons) and charged excitons (trions), which have binding energies in the hundreds and tens of meV, respectively, typically dominate the optical properties of these materials. However, the long-range nature of the interactions between charges represents a significant challenge to the exact calculation of binding energies of complexes larger than the exciton. Here, we demonstrate that the trion binding energy can be efficiently calculated directly from the three-body Schr\"odinger equation in momentum space. Key to this result is a highly accurate way of treating the pole of the electronic interactions at small momentum exchange (i.e., large separation between charges). Our results are in excellent agreement with quantum Monte Carlo calculations, while yielding a substantially larger ratio of the trion to exciton binding energies than obtained in recent variational calculations. Our numerical approach may be extended to a host of different few-body problems in 2D semiconductors, and even potentially to the description of exciton polarons., Comment: 11 pages, 8 figures

In this study we report spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6 , MGTC J100016.84+015133.0 and MGTC J100022.85+031520.4. One such galaxy MGTC J100022.85+031520.4 is reported here for the first time with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544-1088 MHz) observations, and $L$-band (900-1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two different models of spectral ageing, the Jaffe-Perola and Tribble models, using the Broadband Radio Astronomy Tools (\textsc{brats}) software which we find agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, MGTC J100016.84+015133.0 as 47 Myr and MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes which are not accounted for in either the dynamic age or spectral age estimations., Comment: 13 pages, 9 figures. Accepted for publication in MNRAS

A data-driven approach based on unsupervised machine learning is proposed to infer the intrinsic dimensions $m^{\ast}$ of the high-dimensional trajectories of the Fermi-Pasta-Ulam-Tsingou (FPUT) model. Principal component analysis (PCA) is applied to trajectory data consisting of $n_s = 4,000,000$ datapoints, of the FPUT $\beta$ model with $N = 32$ coupled oscillators, revealing a critical relationship between $m^{\ast}$ and the model's nonlinear strength. For weak nonlinearities, $m^{\ast} \ll n$, where $n = 2N$. In contrast, for strong nonlinearities, $m^{\ast} \rightarrow n - 1$, consistently with the ergodic hypothesis. Furthermore, one of the potential limitations of PCA is addressed through an analysis with t-distributed stochastic neighbor embedding ($t$-SNE). Accordingly, we found strong evidence suggesting that the datapoints lie near or on a curved low-dimensional manifold for weak nonlinearities., Comment: 20 pages, 18 figures

We use the Atacama Large sub/Millimetre Array (ALMA) to efficiently observe spectral lines across Bands 3, 4, 5, 6, 7, and 8 at high-resolution (0.5" - 0.1") for 16 bright southern Herschel sources at $1.5 < z < 4.2$. With only six and a half hours of observations, we reveal 66 spectral lines in 17 galaxies. These observations detect emission from CO (3-2) to CO(18-17), as well as atomic ([CI](1-0), (2-1), [OI] 145 $\mu$m and [NII] 205 $\mu$m) lines. Additional molecular lines are seen in emission (${\rm H_2O}$ and ${\rm H_2O^+}$) and absorption (OH$^+$ and CH$^+$). The morphologies based on dust continuum ranges from extended sources to strong lensed galaxies with magnifications between 2 and 30. CO line transitions indicate a diverse set of excitation conditions with a fraction of the sources ($\sim 35$%) showcasing dense, warm gas. The resolved gas to star-formation surface densities vary strongly per source, and suggest that the observed diversity of dusty star-forming galaxies could be a combination of lensed, compact dusty starbursts and extended, potentially-merging galaxies. The predicted gas depletion timescales are consistent with 100 Myr to 1 Gyr, but require efficient fueling from the extended gas reservoirs onto the more central starbursts, in line with the Doppler-shifted absorption lines that indicate inflowing gas for two out of six sources. This pilot paper explores a successful new method of observing spectral lines in large samples of galaxies, supports future studies of larger samples, and finds that the efficiency of this new observational method will be further improved with the planned ALMA Wideband Sensitivity Upgrade., Comment: Accepted for publication in MNRAS. 42 pages, including 20 figures + spectra. Comments and discussion are warmly welcomed

Recent experimental realizations of liquid-liquid phase separation of active liquid crystals have offered an insight into the interaction between phase separation, ubiquitous in soft matter and biology, and chaotic active flows. In this Letter, we use continuum theory to examine phase separation of an active liquid crystal and a passive fluid and report two new results. First, we provide an analytical derivation of the activity-induced suppression of the phase boundary of the coexistence region - a result first reported in simulations and experiments. We show that the shift in the critical point is a result of the balance between self-stirring active flows and phase-separating diffusive fluxes. Second, we show that this same balance is responsible for dramatically changing the morphology of the phase separated state, resulting in the emergence of a new mixed active phase consisting of a dynamical filamentous active network that invades the entire system area, trapping droplets of passive material. This structure exists even for very low volume fractions of active material. Our work provides an important step towards the goal of understanding how to use activity as a new handle for sculpting interfaces., Comment: 6 pages, 4 figures

The key research question we are addressing in this paper, is how local distance information can be integrated into the global structure determination, in the form of network graphs realization for IoT networks. IoT networks will be pervading every walk of life over the next few years with the aim of improving quality of life and enhancing surrounding living conditions, while balancing available resources, like energy and computational power. As we deal with massive number of heterogeneous devices contributing to each IoT network, it is of paramount importance that the IoT network topology can be designed and controlled in such a way that coverage and throughput can be maximized using a minimum number of devices, while tackling challenges like poor link quality and interference. We tackle the above-mentioned problem of topology design and control through our designed graph-realization concept. End-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph. The stitching is done in a way that transmit power and information rate are optimized while reducing error probability.

We develop a microscopic theory for excitons and exciton polaritons in transition metal dichalcogenide (TMD) monolayers under a perpendicular static magnetic field. We obtain numerically exact solutions for the ground and excited states, accounting for the interplay between arbitrarily large magnetic fields and light-matter coupling strengths. This includes the very strong coupling regime, where light-induced modifications of the exciton wavefunction become essential and the approximate coupled oscillator description breaks down. Our results show excellent agreement with recent experimental measurements of the diamagnetic shift of the ground and excited exciton states in WS$_2$, MoS$_2$, MoSe$_2$, and MoTe$_2$ monolayers. For polaritons, we consider experimentally relevant system parameters and demonstrate that the diamagnetic shifts of both the ground and excited states at high magnetic fields exhibit clear signatures of the very strong coupling regime, highlighting the necessity of our microscopic and numerically exact treatment over perturbative approaches. Furthermore, our microscopic approach allows us to evaluate the exciton-exciton and polariton-polariton interaction strengths. Comparing results specific to TMD monolayers with those applicable to quantum wells, we find that variational approaches overestimate the TMD excitons' interaction strength. We also observe that magnetic fields weaken the interaction strength for both excitons and polaritons, with a less pronounced effect in TMDs than in quantum wells, and that light-induced modifications to the matter component in TMD polaritons can enhance interaction strengths beyond those of purely excitonic interactions., Comment: 23 pages, 13 figures

We study convolutional neural networks with monomial activation functions. Specifically, we prove that their parameterization map is regular and is an isomorphism almost everywhere, up to rescaling the filters. By leveraging on tools from algebraic geometry, we explore the geometric properties of the image in function space of this map -- typically referred to as neuromanifold. In particular, we compute the dimension and the degree of the neuromanifold, which measure the expressivity of the model, and describe its singularities. Moreover, for a generic large dataset, we derive an explicit formula that quantifies the number of critical points arising in the optimization of a regression loss.

A one-third monolayer of the heavy metals Sn and Pb deposited on semiconductor substrates can lead to a $\sqrt{3}\times\sqrt{3}$ surface reconstruction, constituting an exciting triangular lattice material platform. A long history of experiments identified charge-ordered and magnetic ground states. These discoveries were accompanied by a decades-long debate of whether electron correlations or other effects involving phonons are the driving force of the symmetry-broken states. The most recent discovery of superconductivity in boron-doped Sn/Si(111) with a $T_c$ between 5K and 9K led to a renewed excitement. Here we revisit the electronic and phononic properties of Sn and Pb adatom triangular lattices on Si(111) and SiC(0001). For all materials we compute relativistic bandstructures using DFT+$U$ where $U$ is only applied to the substrate atoms in order to adjust the band gap to match the experimental value; as a consequence, some of the resulting tight-binding parameters of the metallic surface band differ substantially compared to previous studies. Remarkably, for Pb/SiC(0001) we predict Rashba spin-orbit coupling as large as 45% of the nearest-neighbor hopping energy. In addition, we compute the phonon spectra and electron-phonon coupling constants for all materials, and for Pb/Si(111) even relativistically although the inclusion of spin-orbit coupling has surprisingly little effect on the electron-phonon coupling constant. We conclude that the resulting couplings are too weak to account for electron-phonon mediated superconductivity in any of these materials., Comment: 12 pages, 7 figures, 7 tables

Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image. In this context, depth data could help to disambiguate the 2D information by providing additional constraints about the distance between objects in the scene and the camera. Unfortunately, the acquisition of accurate depth data is limited to indoor spaces and usually is tied to specific depth technologies and devices, thus limiting generalization capabilities. In this paper, we propose a method able to leverage the benefits of depth information without compromising its broader applicability and adaptability in a predominantly RGB-camera-centric landscape. Our approach consists of a heatmap-based 3D pose estimator that, leveraging the paradigm of Privileged Information, is able to hallucinate depth information from the RGB frames given at inference time. More precisely, depth information is used exclusively during training by enforcing our RGB-based hallucination network to learn similar features to a backbone pre-trained only on depth data. This approach proves to be effective even when dealing with limited and small datasets. Experimental results reveal that the paradigm of Privileged Information significantly enhances the model's performance, enabling efficient extraction of depth information by using only RGB images., Comment: ECCV 2024 Workshop T-CAP: TOWARDS A COMPLETE ANALYSIS OF PEOPLE: FINE-GRAINED UNDERSTANDING FOR REAL-WORLD APPLICATIONS

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching.

We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining.

We consider function spaces defined by self-attention networks without normalization, and theoretically analyze their geometry. Since these networks are polynomial, we rely on tools from algebraic geometry. In particular, we study the identifiability of deep attention by providing a description of the generic fibers of the parametrization for an arbitrary number of layers and, as a consequence, compute the dimension of the function space. Additionally, for a single-layer model, we characterize the singular and boundary points. Finally, we formulate a conjectural extension of our results to normalized self-attention networks, prove it for a single layer, and numerically verify it in the deep case.

We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A

The feedback between mechanical and chemical signals plays a key role in controlling many biological processes and collective cell behavior. Here we focus on the emergence of spatiotemporal density waves in a one-dimensional "cell train." Combining a minimal theoretical model with observations in an in vitro experimental system of MDCK epithelial cells confined to a linear pattern, we examine the spontaneous oscillations driven by the feedback between myosin activation and mechanical deformations and their effect on the response of the tissue to externally applied deformations. We show that the nature and frequency of spontaneous oscillations is controlled by the size of the cell train, with a transition from size-dependent standing waves to intrinsic spontaneous waves at the natural frequency of the tissue. The response to external boundary perturbations exhibit a resonance at this natural frequency, providing a possible venue for inferring the mechanochemical couplings that control the tissue behavior from rheological experiments., Comment: 15 pages, 10 figures

Although LLMs and other artificial intelligence systems demonstrate cognitive skills similar to humans, like concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences this paper introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to better understand the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question - is the process of language development and use different in humans and LLMs? - drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative Principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like AI systems., Comment: 15 pages, 4 tables, 2 figures

We study the structure and dynamics of the interface separating a passive fluid from a microtubule-based active fluid. Turbulent-like active flows power giant interfacial fluctuations, which exhibit pronounced asymmetry between regions of positive and negative curvature. Experiments, numerical simulations, and theoretical arguments reveal how the interface breaks up the spatial symmetry of the fundamental bend instability to generate local vortical flows that lead to asymmetric interface fluctuations. The magnitude of interface deformations increases with activity: In the high activity limit, the interface self-folds invaginating passive droplets and generating a foam-like phase, where active fluid is perforated with passive droplets. These results demonstrate how active stresses control the structure, dynamics, and break-up of soft, deformable, and reconfigurable liquid-liquid interfaces., Comment: 26 pages, 22 figures

Active liquid crystals exert nonequilibrium stresses on their surroundings through constant consumption of energy, giving rise to dynamical steady states not present in equilibrium. The paradigmatic example of an active liquid crystal is a suspension of microtubule bundles powered by kinesin motor proteins, which exhibits self-sustained spatiotemporal chaotic flows. This system has been modelled using continuum theories that couple the microtubule orientation to active flows. Recently the focus has shifted to the interfacial properties of mixtures of active liquid crystals and passive fluids. Active/passive interfaces have been shown to support propagating capillary waves in the absence of inertia and offer a promising route for relating experimental parameters to those of the continuum theory. In this paper we report the derivation of a minimal model that captures the linear dynamics of the interface between an active liquid crystal and a passive fluid. We show that the dynamics of the interface, although powered by active flows throughout the bulk, is qualitatively captured by equations that couple non-reciprocally interface height and nematic director at the interface. This minimal model reproduces the dynamical structure factor evaluated from numerical simulations and the qualitative form of the wave dispersion relation seen in experiments., Comment: 14 pages, 7 figures; Updated title, and Fig.5

Consumer IP cameras are now the most widely adopted solution for remote monitoring in various contexts, such as private homes or small offices. While the security of these devices has been scrutinized, most approaches are limited to relatively shallow network-based analyses. In this paper, we discuss a methodology for the security analysis and identification of remotely exploitable vulnerabilities in IP cameras, which includes static and dynamic analyses of executables extracted from IP camera firmware. Compared to existing methodologies, our approach leverages the context of the target device to focus on the identification of malicious invocation sequences that could lead to exploitable vulnerabilities. We demonstrate the application of our methodology by using the Tenda CP3 IP camera as a case study. We identified five novel CVEs, with CVSS scores ranging from 7.5 to 9.8. To partially automate our analysis, we also developed a custom tool based on Ghidra and rhabdomancer., Comment: 8 pages, 3 figures, accepted for publication at the 19th International Workshop on Security (IWSEC 2024)

Hypervelocity stars (HVSs) are stars which have been ejected from the Galactic Centre (GC) at velocities of up to a few thousand km/s. They are tracers of the Galactic potential and can be used to infer properties of the GC, such as the initial-mass function and assembly history. HVSs are rare, however, with only about a dozen promising candidates discovered so far. In this work we make use of a novel, highly efficient method to identify new HVS candidates in Gaia. This method uses the nearly radial trajectories of HVSs to infer their distances and velocities based on their position and Gaia proper motion alone. Through comparison of inferred distances with Gaia parallaxes and photometry we identified 600 HVS candidates with G<20 including the previously discovered S5-HVS1, out of which we obtained ground-based follow-up observations for 196 stars. As we found no new HVSs based on their radial velocity, we used detailed HVS ejection simulations to significantly improve previous HVS ejection rate constraints. In particular, the ejection rate of HVSs more massive than 1 $\mathrm{M_\odot}$ cannot be higher than $10^{-5}$ yr$^{-1}$ at $2\sigma$ significance. Additionally, we predict that there are 5-45 unbound HVSs in the complete Gaia catalogue ($1\sigma$ interval), most of which will be main-sequence stars of a few M$_\odot$ at heliocentric distances of tens to hundreds of kpc. By comparing our results to literature HVS candidates, we find an indication of either a time-dependent ejection rate of HVSs or a non-GC origin of previously identified HVS candidates., Comment: 17 pages, 16 figures, accepted for publication in MNRAS

Single-dish far-infrared (far-IR) and sub-millimetre (sub-mm) point source catalogues and their connections with catalogues at other wavelengths are of paramount importance. However, due to the large mismatch in spatial resolution, cross-matching galaxies at different wavelengths is challenging. This work aims to develop the next-generation deblended far-IR and sub-mm catalogues and present the first application in the COSMOS field. Our progressive deblending used the Bayesian probabilistic framework known as XID+. The deblending started from the Spitzer/MIPS 24 micron data, using an initial prior list composed of sources selected from the COSMOS2020 catalogue and radio catalogues from the VLA and the MeerKAT surveys, based on spectral energy distribution modelling which predicts fluxes of the known sources at the deblending wavelength. To speed up flux prediction, we made use of a neural network-based emulator. After deblending the 24 micron data, we proceeded to the Herschel PACS (100 & 160 micron) and SPIRE wavebands (250, 350 & 500 micron). Each time we constructed a tailor-made prior list based on the predicted fluxes of the known sources. Using simulated far-IR and sub-mm sky, we detailed the performance of our deblending pipeline. After validation with simulations, we then deblended the real observations from 24 to 500 micron and compared with blindly extracted catalogues and previous versions of deblended catalogues. As an additional test, we deblended the SCUBA-2 850 micron map and compared our deblended fluxes with ALMA measurements, which demonstrates a higher level of flux accuracy compared to previous results.We publicly release our XID+ deblended point source catalogues. These deblended long-wavelength data are crucial for studies such as deriving the fraction of dust-obscured star formation and better separation of quiescent galaxies from dusty star-forming galaxies., Comment: 23 pages, 30 figures, accepted for publication in A&A. Catalogues can be downloaded from https://hedam.lam.fr/HELP/dataproducts/dmu26/dmu26_XID+COSMOS2024/

In this paper, we introduce the design of HackCar, a testing platform for replicating attacks and defenses on a generic automotive system without requiring access to a complete vehicle. This platform empowers security researchers to illustrate the consequences of attacks targeting an automotive system on a realistic platform, facilitating the development and testing of security countermeasures against both existing and novel attacks. The HackCar platform is built upon an F1-10th model, to which various automotive-grade microcontrollers are connected through automotive communication protocols. This solution is crafted to be entirely modular, allowing for the creation of diverse test scenarios. Researchers and practitioners can thus develop innovative security solutions while adhering to the constraints of automotive-grade microcontrollers. We showcase our design by comparing it with a real, licensed, and unmodified vehicle. Additionally, we analyze the behavior of the HackCar in both an attack-free scenario and a scenario where an attack on in-vehicle communication is deployed., Comment: 7 pages, 2 figures, accepted for publication at the 35th IEEE Intelligent Vehicles Symposium (IEEE IV 2024)

Strong gravitational lensing is a competitive tool to probe the dark matter and energy content of the universe. However, significant uncertainties can arise from the choice of lens model, and in particular the parameterisation of the line of sight. In this work, we consider the consequences of ignoring the contribution of foreground perturbers in lens modelling. We derive the explicit form of the degeneracy between the foreground shear and the ellipticity of a power law lens, which renders both quantities effectively unmeasurable from strong lensing observables. Nonetheless, we demonstrate that this degeneracy does not affect measurements of the Einstein radius. Foreground tidal effects are also not expected to bias the slope of the potential, and any biases in this slope should not affect the recovery of the Hubble constant. The foreground convergence term adds an additional uncertainty to the measurement of $H_0$, and we show that this uncertainty will be on the order of $1\%$ for lensing systems located along random lines of sight. There is evidence to indicate that the probability of strong lensing is higher towards overdense lines of sight, and this could result in a small systematic bias towards overestimations of $H_0$., Comment: 13 + 8 pages, 2 figures

Reconfigurable Intelligent Surfaces (RIS) are known as a promising technology to improve the performance of wireless communication networks, and have been extensively studied. Movable Antennas (MA) are a novel technology that fully exploits the antenna placement for enhancing the system performance. This article aims at evaluating the impact of transmit power and number of antenna elements on the outage probability performance of an MA-enabled RIS structure (MA-RIS), compared to existing Fixed-Position Antenna RIS (FPA-RIS). The change in geometry caused by the movement of antennas and its implications for the effective number of illuminated elements, are studied for 1D and 2D array structures. Our numerical results confirm the performance advantage provided by MA-RIS, achieving 24\% improvement in outage probability, and 2 dB gain in Signal-to-Noise Ratio (SNR), as compared to FPA-RIS., Comment: 5 pages, 4 figures

We report on deep SCUBA-2 observations at 850$\mu$m and NOEMA spectroscopic measurements at 2 mm of the environment surrounding the luminous, massive ($M_{*} \approx 2 \times 10^{11}$ M$_{\odot}$) Herschel-selected source HerBS-70. This source was revealed by previous NOEMA observations to be a binary system of dusty star-forming galaxies at $z= 2.3$, with the East component (HerBS-70E) hosting an Active Galactic Nucleus (AGN). The SCUBA-2 observations detected, in addition to the binary system, twenty-one sources at $> 3.5 \sigma$ over an area of $\sim 25$ square comoving Mpc with a sensitivity of $\sigma_{850} = 0.75$ mJy. The surface density of continuum sources around HerBS-70 is three times higher than for field galaxies. The NOEMA spectroscopic measurements confirm the protocluster membership of three of the nine brightest sources through their CO(4 - 3) line emission, yielding a volume density 36 times higher than for field galaxies. All five confirmed sub-mm galaxies in the HerBS-70 system have relatively short gas depletion times ($80 - 500$ Myr), indicating the onset of quenching for this protocluster core due to the depletion of gas. The dark matter halo mass of the HerBS-70 system is estimated around $5 \times{} 10^{13}$ M$_{\odot}$, with a projected current-day mass of $10^{15}$ M$_{\odot}$, similar to the local Virgo and Coma clusters. These observations support the claim that DSFGs, in particular the ones with observed multiplicity, can trace cosmic overdensities., Comment: 19 pages, 13 figures, accepted for publication in MNRAS

Quantum state comparison, utilizing metrics like fidelity and trace distance, underpins the assessment of quantum networks within quantum information theory. While recent research has expanded theoretical understanding, incorporating error analysis and scalability considerations remains crucial for practical applications. The primary contribution of this letter is to address these gaps by deriving the novel operational trace distance for multi-node networks, establishing a trace distance vs. fidelity benchmark incorporating error bounds, and bridging quantum operations with tensor network analysis. We further explore the application of tensor network tools to quantum networks, offering new analytical avenues. This comprehensive approach provides a robust framework for evaluating quantum network performance under realistic error conditions, facilitating the development of reliable quantum technologies.

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) -- a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder.

We revisit the problem of controlling Polchinski's equation by the solution of an associate Hamilton-Jacobi equation which determines a norm majorant for the fermionic effective action. This method, referred to as the Majorant Method, was first introduced by D. Brydges and J. Wright in 1988, but its original formulation contains a gap which has never been addressed. We overcome this gap and show that the majorant equation and its existence condition are analogous to the ones originally obtained by Brydges and Wright. As an application of the method, we investigate systems with quartic perturbations., Comment: 23 pages

The recent events affecting global society continuously highlight the need to change the development lifecycle of complex systems by promoting human-centered solutions that increase awareness and ensure critical properties such as security, safety, trust, transparency, and privacy. This fast abstract introduces the Holistic Human-Centered Development Lifecycle (2HCDL) methodology focused on: (i) the enforcement of human values and properties and (ii) the mitigation and prevention of critical issues for more secure, safe, trustworthy, transparent, and private development processes., Comment: S. Bernardi, T. Zoppi (Editors), "Fast Abstracts and Student Forum Proceedings - EDCC 2024 - 19th European Dependable Computing Conference, Leuven, Belgium, 8-11 April 2024"

Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS). Nevertheless, extensive research has shown their vulnerability to adversarial attacks, which involve subtle perturbations to the inputs of the models aimed at compromising their performance. Recent proposals have effectively leveraged Graph Neural Networks (GNN) to produce predictions based also on the structural patterns exhibited by intrusions to enhance the detection robustness. However, the adoption of GNN-based NIDS introduces new types of risks. In this paper, we propose the first formalization of adversarial attacks specifically tailored for GNN in network intrusion detection. Moreover, we outline and model the problem space constraints that attackers need to consider to carry out feasible structural attacks in real-world scenarios. As a final contribution, we conduct an extensive experimental campaign in which we launch the proposed attacks against state-of-the-art GNN-based NIDS. Our findings demonstrate the increased robustness of the models against classical feature-based adversarial attacks, while highlighting their susceptibility to structure-based attacks., Comment: preprint submitted to IEEE TIFS, under review

Multiphase field models have emerged as an important computational tool for understanding biological tissue while resolving single-cell properties. While they have successfully reproduced many experimentally observed behaviors of living tissue, the theoretical underpinnings have not been fully explored. We show that a two-dimensional version of the model, which is commonly employed to study tissue monolayers, can be derived from a three-dimensional version in the presence of a substrate. We also show how viscous forces, which arise from friction between different cells, can be included in the model. Finally, we numerically simulate a tissue monolayer, and find that intercellular friction tends to solidify the tissue., Comment: 16 pages, 4 figures

The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greedy feature selection identifies the most important feature at each step and according to the selected classifier. In the paper, the benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested numerically by considering its application to the problem of predicting geo-effective manifestations of the active Sun.

Scheduling jobs with given processing times on identical parallel machines so as to minimize their total completion time is one of the most basic scheduling problems. We study interesting generalizations of this classical problem involving scenarios. In our model, a scenario is defined as a subset of a predefined and fully specified set of jobs. The aim is to find an assignment of the whole set of jobs to identical parallel machines such that the schedule, obtained for the given scenarios by simply skipping the jobs not in the scenario, optimizes a function of the total completion times over all scenarios. While the underlying scheduling problem without scenarios can be solved efficiently by a simple greedy procedure (SPT rule), scenarios, in general, make the problem NP-hard. We paint an almost complete picture of the evolving complexity landscape, drawing the line between easy and hard. One of our main algorithmic contributions relies on a deep structural result on the maximum imbalance of an optimal schedule, based on a subtle connection to Hilbert bases of a related convex cone.