Search

Cosmic Microwave Background experiments need to measure polarization properties of the incoming radiation very accurately to achieve their scientific goals. As a result of that, it is necessary to properly characterize these instruments. However, there are not natural sources that can be used for this purpose. For this reason, we developed the PROTOtype CALibrator for Cosmology, PROTOCALC, which is a calibrator source designed for the 90 GHz band of these telescopes. This source is purely polarized and the direction of the polarization vector is known with an accuracy better than 0.1 deg. This source flew for the first time in May 2022 showing promising result., Comment: Presented at SPIE Astronomical Telescopes 2024. arXiv admin note: text overlap with arXiv:2207.07595

Teams are the fundamental units propelling innovation and advancing modern science. A rich literature links the fundamental features of teams, such as their size and diversity, to academic success. However, such analyses fail to capture temporal patterns, treating each group of co-authors as a distinct unit and neglecting the existence of persistent collaborations. By contrast, teams are dynamical entities, made of core members who consistently work together, surrounded by transient members who sporadically participate. Leveraging on a large dataset of over 205 million scientific papers published since 1900, we extract 511,550 core teams of statistically significant persistent collaborations of pairs and larger groups of scientists. We look into `team careers' investigating their trajectories in time, characterizing their formation, productivity and eventual dissolution. We characterize team composition along multiple dimensions, including age, academic affiliation and scientific disciplines. Finally, we investigate the academic impact of persistent collaborations, hallmarking the key compositional features underlying their success. Our work sheds light on the nature of persistent teams, informing researchers, institutions and funding agencies about the dynamics of their formation, evolution and success., Comment: 18 pages, 13 figures

In this work, the performance of SensL MicroFC-60035 SiPM devices was studied during a 1460-day mission in Low Earth Orbit (LEO) using the LabOSat-01 characterization payload. Two of these platforms, carrying two SiPMs each, were integrated into the \~NuSat-7 satellite (COSPAR-ID: 2020-003B). Analysis revealed that these SiPMs experienced an increase in dark current over time due to damage from trapped and solar proton radiation. The total ionizing dose received by the payload and the SiPMs was measured using p-MOSFET dosimeters, with a resulting value of 5 Gy, or a 1 MeV neutron equivalent fluence of $\phi_n = 5 \cdot 10^9$ n/cm$^2$. The dark current was observed to increase up to 500 times. Parameters such as Gain and Photon Detection Efficiency remained unchanged throughout the mission. These findings align with previous performance reports involving different SiPMs irradiated with various particles and energies.

We use the motivic Greenlees spectral sequence from arXiv:2408.00338 to compute Hochschild homology in the stable motivic homotopy category over an algebraically closed field. Our target is $MHH(M\mathbb{Z}/p)/\tau^{p-1}$, where $M\mathbb{Z}/p$ is modulo $p$ motivic cohomology, $p$ a prime number different from the characteristic of the base., Comment: 87 pages; for printing, we recommend to recompile choosing the cropped diagrams in main.tex

Open-vocabulary 3D segmentation enables the exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on identifying object-level instances in a scene. However, they face challenges when it comes to understanding more fine-grained scene entities such as object parts, or regions described by generic attributes. In this work, we introduce Search3D, an approach that builds a hierarchical open-vocabulary 3D scene representation, enabling the search for entities at varying levels of granularity: fine-grained object parts, entire objects, or regions described by attributes like materials. Our method aims to expand the capabilities of open vocabulary instance-level 3D segmentation by shifting towards a more flexible open-vocabulary 3D search setting less anchored to explicit object-centric queries, compared to prior work. To ensure a systematic evaluation, we also contribute a scene-scale open-vocabulary 3D part segmentation benchmark based on MultiScan, along with a set of open-vocabulary fine-grained part annotations on ScanNet++. We verify the effectiveness of Search3D across several tasks, demonstrating that our approach outperforms baselines in scene-scale open-vocabulary 3D part segmentation, while maintaining strong performance in segmenting 3D objects and materials., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We examine the fluid flow forced by precession of a rotating cylindrical container using numerical simulations and experimental flow measurements with ultrasonic Doppler velocimetry (UDV). The analysis is based on the decomposition of the flow field into contributions with distinct azimuthal symmetry or analytically known inertial modes and the corresponding calculation of their amplitudes. We show that the predominant fraction of the kinetic energy of the precession-driven fluid flow is contained only within a few large-scale modes. The most striking observation shown by simulations and experiments is the transition from a flow dominated by large-scale structures to a more turbulent behaviour with the small-scale fluctuations becoming increasingly important. At a fixed rotation frequency (parametrized by the Reynolds number, ${\rm{Re}}$) this transition occurs when a critical precession ratio is exceeded and consists of a two-stage collapse of the directly driven flow going along with a massive modification of the azimuthal circulation (the zonal flow) and the appearance of an axisymmetric double-roll mode limited to a narrow range of precession ratios. A similar behaviour is found in experiments which make it possible to follow the transition up to Reynolds numbers of ${\rm{Re}}\approx 2\times 10^6$. We find that the critical precession ratio decreases with rotation, initially showing a particular scaling $\propto {\rm{Re}}^{-\frac{1}{5}}$ but developing an asymptotic behaviour for ${\rm{Re}}\gtrsim 10^5$ which might be explained by the onset of turbulence in boundary layers., Comment: 2024, 29 pages, 16 figures, accepted for publication in Journal of Fluid Mechanics

The exceptional magnetic, optical and phononic properties of Yttrium Iron Garnet (YIG) make it unique for spin-wave based and photonic applications. Yet, nanostructuring crystalline YIG and manipulating its magnetism in a non-destructive way is an outstanding challenge, and so far mostly limited to two-dimensional capabilities. Here, we show that irradiation of single-crystal YIG films with a focused UV laser drives a stable, giant enhancement of the perpendicular magnetic anisotropy, preserving the crystalline quality. This modulation is highly confined at the nanoscale in both the lateral and vertical directions, and its extension within the volume can be finely tuned with a continuous depth-control. By harnessing these three-dimensional anisotropy profiles, we demonstrate a large tuning of the spin-wave band structure, volume spatial localization, and non-reciprocity, realizing proof-of-principle 3D magnonic crystals. This straightforward, single-step, laser nanofabrication of three-dimensional magnetic systems based on crystalline YIG thin films opens the way to design novel functions in magnonic and magneto-optic devices., Comment: 17 pages, 5 figures

Thermoelectric transport has been widely used to study Abrikosov vortex dynamics in unconventional superconductors. However, only a few thermoelectric studies have been conducted near the dimensional crossover that occurs when the vortex-vortex interaction length scale becomes comparable to the sample size. Here we report the effects of finite size on the dissipation mechanisms of the Nernst effect in the optimally doped $\text{Bi}_{2}\text{Sr}_{2}\text{CaCu}_{2}\text{O}_{8+x}$ high-temperature superconductor, down to the atomic length limit. To access this regime, we develop a new generation of thermoelectric chips based on silicon nitride microprinted circuit boards. These chips ensure optimized signals while preventing sample deterioration. Our results demonstrate that lateral confinement at the nanoscale can effectively reduce vortex dissipation. Investigating vortex dissipation at the micro- and nano-scale is essential for creating stable, miniaturized superconducting circuits.

The notion that collaborative machine learning can ensure privacy by just withholding the raw data is widely acknowledged to be flawed. Over the past seven years, the literature has revealed several privacy attacks that enable adversaries to extract information about a model's training dataset by exploiting access to model parameters during or after training. In this work, we study privacy attacks in the gray-box setting, where the attacker has only limited access - in terms of view and actions - to the model. The findings of our investigation provide new insights for the development of privacy-preserving collaborative learning solutions. We deploy SmartCryptNN, a framework that tailors homomorphic encryption to protect the portions of the model posing higher privacy risks. Our solution offers a trade-off between privacy and efficiency, which varies based on the extent and selection of the model components we choose to protect. We explore it on dense neural networks, where through extensive evaluation of diverse datasets and architectures, we uncover instances where a favorable sweet spot in the trade-off can be achieved by safeguarding only a single layer of the network. In one of such instances, our approach trains ~4 times faster compared to fully encrypted solutions, while reducing membership leakage by 17.8 times compared to plaintext solutions., Comment: 19 pages, 7 figures, in submission

The ultraslow dynamics of glass-formers has been explained by two views considered as mutually exclusive: one invokes locally hindered mobility, the other rests on the complexity of the configuration space. Here we demonstrate that the evolution responds strongly to the details of the dynamics by changing the speed of time-flow: it has time-reparametrization softness. This finding reconciles both views: while local constraints reparametrize the flow of time, the global landscape determines relationships between different correlations at the same times. We show that modern algorithms developed to accelerate the relaxation to equilibrium act by changing the time reparametrization. Their success thus relies on their ability to exploit reparametrization softness. We conjecture that these results extend beyond the realm of glasses to the optimization of more general constraint satisfaction problems and to broader classes of algorithms.

Mult-layered meta-optics have enabled complex wavefront shaping beyond their single layer counterpart owing to the additional design variables afforded by each plane. For instance, complex amplitude modulation, generalized polarization transformations, and wide field of view are key attributes that fundamentally require multi-plane wavefront matching. Nevertheless, existing embodiments of bilayer metasurfaces have relied on configurations which suffer from Fresnel reflections, low mode confinement, or undesired resonances which compromise the intended response. Here, we introduce bilayer metasurfaces made of free-standing meta-atoms working in the visible spectrum. We demonstrate their use in wavefront shaping of linearly polarized light using pure geometric phase with diffraction efficiency of 80 % expanding previous literature on Pancharatnam-Berry phase metasurfaces which rely on circularly or elliptically polarized illumination. The fabrication relies on a two-step lithography and selective development processes which yield free standing, bilayer stacked metasurfaces, of 1200 nm total thickness. The metasurfaces comprise TiO2 nanofins with vertical side walls. Our work advances the nanofabrication of compound meta-optics and inspires new directions in wavefront shaping, metasurface integration, and polarization control., Comment: A. H. Dorrah, J. -S. Park, and A. Palmieri contributed equally to this work

In recent years, drone detection has quickly become a subject of extreme interest: the potential for fast-moving objects of contained dimensions to be used for malicious intents or even terrorist attacks has posed attention to the necessity for precise and resilient systems for detecting and identifying such elements. While extensive literature and works exist on object detection based on RGB data, it is also critical to recognize the limits of such modality when applied to UAVs detection. Detecting drones indeed poses several challenges such as fast-moving objects and scenes with a high dynamic range or, even worse, scarce illumination levels. Neuromorphic cameras, on the other hand, can retain precise and rich spatio-temporal information in situations that are challenging for RGB cameras. They are resilient to both high-speed moving objects and scarce illumination settings, while prone to suffer a rapid loss of information when the objects in the scene are static. In this context, we present a novel model for integrating both domains together, leveraging multimodal data to take advantage of the best of both worlds. To this end, we also release NeRDD (Neuromorphic-RGB Drone Detection), a novel spatio-temporally synchronized Event-RGB Drone detection dataset of more than 3.5 hours of multimodal annotated recordings., Comment: Accepted at NeVi Workshop at ECCV24

3D shape completion is traditionally solved using supervised training or by distribution learning on complete shape examples. Recently self-supervised learning approaches that do not require any complete 3D shape examples have gained more interests. In this paper, we propose a non-adversarial self-supervised approach for the shape completion task. Our first finding is that completion problems can be formulated as an involutory function trivially, which implies a special constraint on the completion function G, such that G(G(X)) = X. Our second constraint on self-supervised shape completion relies on the fact that shape completion becomes easier to solve with correspondences and similarly, completion can simplify the correspondences problem. We formulate a consistency measure in the canonical space in order to supervise the completion function. We efficiently optimize the completion and correspondence modules using "freeze and alternate" strategy. The overall approach performs well for rigid shapes in a category as well as dynamic non-rigid shapes. We ablate our design choices and compare our solution against state-of-the-art methods, showing remarkable accuracy approaching supervised accuracy in some cases., Comment: ECCV 2024

Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on $\mathbb{X}$ from May 2024. This reveals a network of coordinated inauthentic actors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts to disseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation: The links shared by this network frequently direct users to other social media platforms or suspicious websites featuring low-quality political content and, in turn, promoting the same $\mathbb{X}$ and YouTube accounts. Members of this network also shared deceptive images generated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance. While $\mathbb{X}$ has suspended a subset of these accounts, more than 75% of the coordinated network remains active. Our findings underscore the critical role of developing computational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications of these techniques to detect IOs across the wider Web., Comment: The 2024 Election Integrity Initiative: HUMANS Lab - Working Paper No. 2024.4 - University of Southern California

We use the rms and lag spectra of the type-C quasi-periodic oscillation (QPO) to study the properties of the Comptonisation region (aka corona) during the low/hard and hard-intermediate states of the main outburst and reflare of MAXI J1348$-$630. We simultaneously fit the time-averaged energy spectrum of the source and the fractional rms and phase-lag spectra of the QPO with the time-dependent Comptonization model vKompth. The data can be explained by two physically connected coronae interacting with the accretion disc via a feedback loop of X-ray photons. The best-fitting model consists of a corona of $\sim$10$^3$ km located at the inner edge of the disc and a second corona of $\sim$10$^4$ km horizontally extended and covering the inner parts of the accretion disc. The properties of both coronae during the reflare are similar to those during the low/hard state of the main outburst, reinforcing the idea that both the outburst and the reflare are driven by the same physical mechanisms. We combine our results for the type-C QPO with those from previous work focused on the study of type-A and type-B QPOs with the same model to study the evolution of the geometry of the corona through the whole outburst, including the reflare of MAXI J1348$-$630. Finally, we show that the sudden increase in the phase-lag frequency spectrum and the sharp drop in the coherence function previously observed in MAXI J1348$-$630 are due to the type-C QPO during the decay of the outburst and can be explained in terms of the geometry of the coronae., Comment: 18 pages, 8 figures, 1 table. Submitted to ApJ

Palm distributions are critical in the study of point processes. In the present paper we focus on a point process $\Phi$ defined as the superposition, i.e., sum, of two independent point processes, say $\Phi = \Phi_1 + \Phi_2$, and we characterize its Palm distribution. In particular, we show that the Palm distribution of $\Phi$ admits a simple mixture representation depending only on the Palm distribution of $\Phi_j$, as $j=1, 2$, and the associated moment measures. Extensions to the superposition of multiple point processes, and higher order Palm distributions, are treated analogously.

High-resolution flood probability maps are essential for addressing the limitations of existing flood risk assessment approaches but are often limited by the availability of historical event data. Also, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort inhibiting the feasibility. To address this gap, this study introduces Flood-Precip GAN (Flood-Precipitation Generative Adversarial Network), a novel methodology that leverages generative machine learning to simulate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Flood-Precip GAN begins with training a cell-wise depth estimator using a limited number of physics-based model-generated precipitation-flood events. This model, which emphasizes precipitation-based features, outperforms universal models. Subsequently, a Generative Adversarial Network (GAN) with constraints is employed to conditionally generate synthetic precipitation records. Strategic thresholds are established to filter these records, ensuring close alignment with true precipitation patterns. For each cell, synthetic events are smoothed using a K-nearest neighbors algorithm and processed through the depth estimator to derive synthetic depth distributions. By iterating this procedure and after generating 10,000 synthetic precipitation-flood events, we construct flood probability maps in various formats, considering different inundation depths. Validation through similarity and correlation metrics confirms the fidelity of the synthetic depth distributions relative to true data. Flood-Precip GAN provides a scalable solution for generating synthetic flood depth data needed to create high-resolution flood probability maps, significantly enhancing flood preparedness and mitigation efforts.

We analyse high-resolution ESPaDOnS/CFHT spectra of 20 very metal-poor stars ([Fe/H]~$<-2.0$) in the solar neighbourhood (within $\sim2$ kpc) selected to be on planar orbits (maximum height of $<4$ kpc). Targets include 11 prograde and 9 retrograde stars, spanning a wide range of eccentricities ($0.20-0.95$). Their chemical abundances are consistent with those observed in the Galactic halo but show a smaller spread, with no notable difference between progrades and retrogrades. This suggests a common chemical evolution and likely a shared formation site (except for one star). In this case, chemical evolution models indicate that the formation site would have had a baryonic mass of $\sim1.4\times10^9\msun$, similar to classical dwarf galaxies. High-energy supernovae and hypernovae are needed to reproduce the [X/Fe] up to the Fe-peak, while fast-rotating massive stars and neutron star merger events explain the [X/Fe] of the neutron-capture elements. The absence of Type Ia supernova signatures suggests a star formation duration of $<1$Gyr. Cosmological zoom-in simulations support the scenario that an in-plane infall of a single system could disperse stars over a wide range of angular momenta during the early Galactic assembly. We propose that these stars originated in a proto-Galactic building block, which we name \textit{Loki}. Less likely, if progrades and retrogrades formed in two different systems, their chemical evolution must have been very similar, with a combined baryonic mass twice that of a single system. The low number of targets and their limited metallicity range prevent us to exclude if these stars share a common progenitor with other detected structures, like GSE. A comparison (primarily [$\alpha$/Fe]) with other VMPs moving in planar orbits suggests multiple systems contributed to the Galactic planar population, presenting some differences in their kinematical parameters., Comment: Submitted to A&A, 21 pages, 14 Figures

On the analytic side, we prove the quantum ergodicity (QE) of Hamiltonian operators on certain series of unitary flat bundles, using mixed quantization techniques. On the dynamical side, we introduce a new family of partially hyperbolic flow associated with QE and establish its ergodicity.

It is usually assumed that the memory of any thermo-mechanical protocol applied to a glass can be erased by heating the material in the supercooled liquid. While this is true for thermally treated amorphous solids, we show that hydrostatic compression can irreversibly modify the atomic motion, thermodynamic state and structure of a prototypical metallic glass-former, in a way which depends on the degree of ergodicity reached by the material during compression. While enhanced kinetic and thermodynamic stability can be obtained by quenching the dense liquid, high-pressure annealing in the glass leads to thermal rejuvenation and complex structural rearrangements at the level of the short and medium range order. When heated above their glass transition temperature, these compressed glasses do not convert into the pristine supercooled liquid but rather transform into different systems, challenging the generally accepted idea of an equilibrium recovery controlled solely by the microscopic $\alpha$-relaxation process.

Jupiter's icy moon Ganymede has a tenuous exosphere produced by sputtering and possibly sublimation of water ice. To date, only atomic hydrogen and oxygen have been directly detected in this exosphere. Here, we present observations of Ganymede's CO$_2$ exosphere obtained with the James Webb Space Telescope. CO$_2$ gas is observed over different terrain types, mainly over those exposed to intense Jovian plasma irradiation, as well as over some bright or dark terrains. Despite warm surface temperatures, the CO$_2$ abundance over equatorial subsolar regions is low. CO$_2$ vapor has the highest abundance over the north polar cap of the leading hemisphere, reaching a surface pressure of 1 pbar. From modeling we show that the local enhancement observed near 12 h local time in this region can be explained by the presence of cold traps enabling CO$_2$ adsorption. However, whether the release mechanism in this high-latitude region is sputtering or sublimation remains unclear. The north polar cap of the leading hemisphere also has unique surface-ice properties, probably linked to the presence of the large atmospheric CO2 excess over this region. These CO2 molecules might have been initially released in the atmosphere after the radiolysis of CO$_2$ precursors, or from the sputtering of CO$_2$ embedded in the H$_2$O ice bedrock. Dark terrains (regiones), more widespread on the north versus south polar regions, possibly harbor CO$_2$ precursors. CO$_2$ molecules would then be redistributed via cold trapping on ice-rich terrains of the polar cap and be diurnally released and redeposited on these terrains. Ganymede's CO$_2$ exosphere highlights the complexity of surface-atmosphere interactions on Jupiter's icy Galilean moons., Comment: 21 pages, 21 figures, Accepted as a Letter in Astronomy and Astrophysics

Motivated by the Gottesman-Knill theorem, we present a detailed study of the quantum complexity of $p$-shell and $sd$-shell nuclei. Valence-space nuclear shell-model wavefunctions generated by the BIGSTICK code are mapped to qubit registers using the Jordan-Wigner mapping (12 qubits for the $p$-shell and 24 qubits for the $sd$-shell), from which measures of the many-body entanglement ($n$-tangles) and magic (non-stabilizerness) are determined. While exact evaluations of these measures are possible for nuclei with a modest number of active nucleons, Monte Carlo simulations are required for the more complex nuclei. The broadly-applicable Pauli-String $IZ$ exact (PSIZe-) MCMC technique is introduced to accelerate the evaluation of measures of magic in deformed nuclei (with hierarchical wavefunctions), by factors of $\sim 8$ for some nuclei. Significant multi-nucleon entanglement is found in the $sd$-shell, dominated by proton-neutron configurations, along with significant measures of magic. This is evident not only for the deformed states, but also for nuclei on the path to instability via regions of shape coexistence and level inversion. These results indicate that quantum-computing resources will accelerate precision simulations of such nuclei and beyond., Comment: 36 pages, 13 figures

Open systems that are weakly coupled to a thermal environment and driven by fast, periodically oscillating fields are commonly assumed to approach an equilibrium-like steady state with respect to a truncated Floquet-Magnus Hamiltonian. Using a general argument based on Fermi's golden rule, we show that such Floquet-Gibbs states emerge naturally in periodically modulated Rydberg atomic systems, whose lab-frame Hamiltonian is a quasiperiodic function of time. Our approach applies as long as the inherent Bohr frequencies of the system, the modulation frequency and the frequency of the driving laser, which is necessary to uphold high-lying Rydberg excitations, are well separated. To corroborate our analytical results, we analyze a realistic model of up to five interacting Rydberg atoms with periodically changing detuning. We demonstrate numerically that the second-order Floquet-Gibbs state of this system is essentially indistinguishable from the steady state of the corresponding Redfield equation if the modulation and driving frequencies are sufficiently large., Comment: 8 + 4 pages, 2 figures

This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of \modelname is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model's accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, \modelname addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets; \modelname outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a $10\times$ reduction in RMSE and a $3.14\times$ increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model's overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at \url{https://github.com/rom42pla/ntvit}., Comment: ECCV24 Workshop on Synthetic Data for Computer Vision

Current images of the supermassive black hole (SMBH) candidates at the center of our Galaxy and M87 have opened an unprecedented era for studying strong gravity and the nature of relativistic sources. Very-long-baseline interferometry (VLBI) data show images consistent with a central SMBH within General Relativity (GR). However, it is essential to consider whether other well-motivated dark compact objects within GR could produce similar images. Recent studies have shown that dark matter (DM) halos modeled as self-gravitating systems of neutral fermions can harbor very dense fermionic cores at their centers, which can mimic the spacetime features of a black hole (BH). Such dense, horizonless DM cores can satisfy the observational constraints: they can be supermassive and compact and lack a hard surface. We investigate whether such cores can produce similar observational signatures to those of BHs when illuminated by an accretion disk. We compute images and spectra of the fermion cores with a general-relativistic ray tracing technique, assuming the radiation originates from standard $\alpha$ disks, which are self-consistently solved within the current DM framework. Our simulated images possess a central brightness depression surrounded by a ring-like feature, resembling what is expected in the BH scenario. For Milky Way-like halos, the central brightness depressions have diameters down to $\sim 35\, \mu$as as measured from a distance of approximately $8\,$kpc. Finally, we show that the DM cores do not possess photon rings, a key difference from the BH paradigm, which could help discriminate between the models., Comment: 11 pages, 7 figures. Accepted for publication in MNRAS

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

We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories by aggregating the individual classifications, and no category can be left empty. An aggregator satisfies \emph{Expertise} if individuals are decisive either over the classification of a given object, or the classification into a given category. We show that requiring an aggregator to satisfy \emph{Expertise} and be either unanimous or independent leads to numerous impossibility results.

Diffusion models have significantly advanced generative AI, but they encounter difficulties when generating complex combinations of multiple objects. As the final result heavily depends on the initial seed, accurately ensuring the desired output can require multiple iterations of the generation process. This repetition not only leads to a waste of time but also increases energy consumption, echoing the challenges of efficiency and accuracy in complex generative tasks. To tackle this issue, we introduce HEaD (Hallucination Early Detection), a new paradigm designed to swiftly detect incorrect generations at the beginning of the diffusion process. The HEaD pipeline combines cross-attention maps with a new indicator, the Predicted Final Image, to forecast the final outcome by leveraging the information available at early stages of the generation process. We demonstrate that using HEaD saves computational resources and accelerates the generation process to get a complete image, i.e. an image where all requested objects are accurately depicted. Our findings reveal that HEaD can save up to 12% of the generation time on a two objects scenario and underscore the importance of early detection mechanisms in generative models., Comment: Accepted at ECCV Workshop 2024

We study numerically the Hessian of low-lying minima of vector spin glass models defined on random regular graphs. We consider the two-component (XY) and three-component (Heisenberg) spin glasses at zero temperature, subjected to the action of a randomly oriented external field. Varying the intensity of the external field, these models undergo a zero temperature phase transition from a paramagnet at high field to a spin glass at low field. We study how the spectral properties of the Hessian depend on the magnetic field. In particular, we study the shape of the spectrum at low frequency and the localization properties of low energy eigenvectors across the transition. We find that in both phases the edge of the spectral density behaves as $\lambda^{3/2}$: such a behavior rules out the presence of a diverging spin-glass susceptibility $\chi_{SG}=\langle 1/\lambda^2 \rangle$. As to low energy eigenvectors, we find that the softest eigenmodes are always localized in both phases of the two models. However, by studying in detail the geometry of low energy eigenmodes across different energy scales close to the lower edge of the spectrum, we find a different behavior for the two models at the transition: in the XY case, low energy modes are typically localized; at variance, in the Heisenberg case low-energy eigenmodes with a multi-modal structure (sort of ``delocalization'') appear at an energy scale that vanishes in the infinite size limit. These geometrically non-trivial excitations, which we call Concentrated and Delocalised Low Energy Modes (CDLEM), coexist with trivially localised excitations: we interpret their existence as a sign of critical behavior related to the onset of the spin glass phase., Comment: 15 pages, 8 figures

This paper proposes a versatile graph-based lifelong localization framework, LiLoc, which enhances its timeliness by maintaining a single central session while improves the accuracy through multi-modal factors between the central and subsidiary sessions. First, an adaptive submap joining strategy is employed to generate prior submaps (keyframes and poses) for the central session, and to provide priors for subsidiaries when constraints are needed for robust localization. Next, a coarse-to-fine pose initialization for subsidiary sessions is performed using vertical recognition and ICP refinement in the global coordinate frame. To elevate the accuracy of subsequent localization, we propose an egocentric factor graph (EFG) module that integrates the IMU preintegration, LiDAR odometry and scan match factors in a joint optimization manner. Specifically, the scan match factors are constructed by a novel propagation model that efficiently distributes the prior constrains as edges to the relevant prior pose nodes, weighted by noises based on keyframe registration errors. Additionally, the framework supports flexible switching between two modes: relocalization (RLM) and incremental localization (ILM) based on the proposed overlap-based mechanism to select or update the prior submaps from central session. The proposed LiLoc is tested on public and custom datasets, demonstrating accurate localization performance against state-of-the-art methods. Our codes will be publicly available on https://github.com/Yixin-F/LiLoc., Comment: conference

Traditional approaches for analyzing RGB frames are capable of providing a fine-grained understanding of a face from different angles by inferring emotions, poses, shapes, landmarks. However, when it comes to subtle movements standard RGB cameras might fall behind due to their latency, making it hard to detect micro-movements that carry highly informative cues to infer the true emotions of a subject. To address this issue, the usage of event cameras to analyze faces is gaining increasing interest. Nonetheless, all the expertise matured for RGB processing is not directly transferrable to neuromorphic data due to a strong domain shift and intrinsic differences in how data is represented. The lack of labeled data can be considered one of the main causes of this gap, yet gathering data is harder in the event domain since it cannot be crawled from the web and labeling frames should take into account event aggregation rates and the fact that static parts might not be visible in certain frames. In this paper, we first present FACEMORPHIC, a multimodal temporally synchronized face dataset comprising both RGB videos and event streams. The data is labeled at a video level with facial Action Units and also contains streams collected with a variety of applications in mind, ranging from 3D shape estimation to lip-reading. We then show how temporal synchronization can allow effective neuromorphic face analysis without the need to manually annotate videos: we instead leverage cross-modal supervision bridging the domain gap by representing face shapes in a 3D space., Comment: Accepted for publication at the ECCV 2024 workshop on Neuromorphic Vision: Advantages and Applications of Event Cameras (NEVI)

In the rapidly evolving field of online fashion shopping, the need for more personalized and interactive image retrieval systems has become paramount. Existing methods often struggle with precisely manipulating specific garment attributes without inadvertently affecting others. To address this challenge, we propose GAMMA (Garment Attribute Manipulation with Multi-level Attention), a novel framework that integrates attribute-disentangled representations with a multi-stage attention-based architecture. GAMMA enables targeted manipulation of fashion image attributes, allowing users to refine their searches with high accuracy. By leveraging a dual-encoder Transformer and memory block, our model achieves state-of-the-art performance on popular datasets like Shopping100k and DeepFashion., Comment: Accepted for publication at the ECCV 2024 workshop FashionAI

Two-dimensional crystals stack together through weak van der Waals (vdW) forces, offering unlimited possibilities to play with layer number, order and twist angle in vdW heterostructures (HSs). The realisation of high-performance optoelectronic devices, however, requires the achievement of specific band alignments, $k$-space matching between conduction band minima and valence band maxima, as well as efficient charge transfer between the constituent layers. Fine tuning mechanisms to design ideal HSs are lacking. Here, we show that layer-selective strain engineering can be exploited as an extra degree of freedom in vdW HSs to tailor their band alignment and optical properties. To that end, strain is selectively applied to MS$_2$ (M=Mo,W) monolayers in InSe/MS$_2$ HSs. This triggers a giant PL enhancement of the highly tuneable but weakly emitting InSe by one to three orders of magnitude. Resonant PL excitation measurements, supported by first-principle calculations, provide evidence of a strain-activated direct charge transfer from the MS$_2$ MLs toward InSe. This significant emission enhancement achieved for InSe widens its range of applications for optoelectronics.

End-to-end neural diarization has evolved considerably over the past few years, but data scarcity is still a major obstacle for further improvements. Self-supervised learning methods such as WavLM have shown promising performance on several downstream tasks, but their application on speaker diarization is somehow limited. In this work, we explore using WavLM to alleviate the problem of data scarcity for neural diarization training. We use the same pipeline as Pyannote and improve the local end-to-end neural diarization with WavLM and Conformer. Experiments on far-field AMI, AISHELL-4, and AliMeeting datasets show that our method substantially outperforms the Pyannote baseline and achieves performance comparable to the state-of-the-art results on AMI and AISHELL-4. In addition, by analyzing the system performance under different data quantity scenarios, we show that WavLM representations are much more robust against data scarcity than filterbank features, enabling less data hungry training strategies. Furthermore, we found that simulated data, usually used to train endto-end diarization models, does not help when using WavLM in our experiments. Additionally, we also evaluate our model on the recent CHiME8 NOTSOFAR-1 task where it achieves better performance than the Pyannote baseline. Our source code is publicly available at https://github.com/BUTSpeechFIT/DiariZen., Comment: Submitted to ICASSP 2025

We prove that two natural Markov chains on the set of monotone paths in a strip mix slowly. To do so, we make novel use of the theory of non-positively curved (CAT(0)) cubical complexes to detect small bottlenecks in many graphs of combinatorial interest. Along the way, we give a formula for the number c_m(n) of monotone paths of length n in a strip of height m. In particular we compute the exponential growth constant of c_m(n) for arbitrary m, generalizing results of Williams for m=2, 3., Comment: 30 pages, 9 figures

The paper presents a novel approach for unsupervised techniques in the field of clustering. A new method is proposed to enhance existing literature models using the proper Bayesian bootstrap to improve results in terms of robustness and interpretability. Our approach is organized in two steps: k-means clustering is used for prior elicitation, then proper Bayesian bootstrap is applied as resampling method in an ensemble clustering approach. Results are analyzed introducing measures of uncertainty based on Shannon entropy. The proposal provides clear indication on the optimal number of clusters, as well as a better representation of the clustered data. Empirical results are provided on simulated data showing the methodological and empirical advances obtained.

Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation approach for fast performance models to accurately estimate the latency of a DNN mapped onto systematically modeled and concisely described accelerator architectures. Using our accelerator architecture description method, we modeled representative DNN accelerators such as Gemmini, UltraTrail, Plasticine-derived, and a parameterizable systolic array. Together with DNN mappings for those modeled architectures, we perform a combined DNN/hardware dependency graph analysis, which enables us, in the best case, to evaluate only 154 loop kernel iterations to estimate the performance for 4.19 billion instructions achieving a significant speedup. We outperform regression and analytical models in terms of mean absolute percentage error (MAPE) compared to simulation results, while being several magnitudes faster than an RTL simulation., Comment: Accepted version for: ACM Transactions on Embedded Computing Systems

We consider a discrete-time Markovian random walk with resets on a connected undirected network. The resets, in which the walker is relocated to randomly chosen nodes, are governed by an independent discrete-time renewal process. Some nodes of the network are target nodes, and we focus on the statistics of first hitting of these nodes. In the non-Markov case of the renewal process, we consider both light- and fat-tailed inter-reset distributions. We derive the propagator matrix in terms of discrete backward recurrence time PDFs and in the light-tailed case we show the existence of a non-equilibrium steady state. In order to tackle the non-Markov scenario, we derive a defective propagator matrix which describes an auxiliary walk characterized by killing the walker as soon as it hits target nodes. This propagator provides the information on the mean first passage statistics to the target nodes. We establish sufficient conditions for ergodicity of the walk under resetting. Furthermore, we discuss a generic resetting mechanism for which the walk is non-ergodic. Finally, we analyze inter-reset time distributions with infinite mean where we focus on the Sibuya case. We apply these results to study the mean first passage times for Markovian and non-Markovian (Sibuya) renewal resetting protocols in realizations of Watts-Strogatz and Barab\'asi-Albert random graphs. We show non trivial behavior of the dependence of the mean first passage time on the proportions of the relocation nodes, target nodes and of the resetting rates. It turns out that, in the large-world case of the Watts-Strogatz graph, the efficiency of a random searcher particularly benefits from the presence of resets.

While auto-associative neural networks (e.g., the Hopfield model implementing the standard Hebbian prescription for learning) play as the reference setting for pattern recognition and associative memory in statistical mechanics, hetero-associative extensions (despite much less investigated) display richer emergent computational skills. Here we study the simplest generalization of the Kosko's Bidirectional Associative Memory (BAM), namely a Three-directional Associative Memory (TAM), that is a tripartite neural network equipped with generalized Hebbian weights. We study its information processing capabilities analytically (via statistical mechanics and signal-to-noise techniques) and computationally (via Monte Carlo simulations). Confined to the replica symmetric description, we provide phase diagrams for this network in the space of the control parameters, highlighting the existence of a region where the machine can successful perform recognition as well as other tasks. For instance, it can perform pattern disentanglement, namely when inputted with a mixture of patterns, the network is able to return the original patterns, namely to disentangle the signal's components. Further, they can also perform retrieval of (Markovian) sequences of patterns and they can also disentangle mixtures of periodic patterns: should these mixtures be sequences that combine patterns alternating at different frequencies, these hetero-associative networks can perform generalized frequency modulation by using the slowly variable sequence of patterns as the base-band signal and the fast one as the information carrier.

Quantum entanglement between the spin, orbital and lattice degrees of freedom in condensed matter systems can emerge due to an interplay between spin-orbit and vibronic interactions. Heavy transition metal ions decorated on a face-centered cubic lattice, for example in $5d^1$ double perovskites, are particularly suited to support these quantum entangled states, but direct evidence has not yet been presented. In this work, we report additional peaks in the low-energy spectra of a $5d^1$ double perovskite, Ba$_2$CaReO$_6$, which cannot be explained by adopting a purely classical description of lattice vibrations. Instead, our theoretical analysis demonstrates that these spectroscopic signatures are characteristic of orbital-lattice entangled states in Ba$_2$CaReO$_6$. Crucially, both theory and experiment demonstrate that these quantum-entangled states persist to low temperatures, despite the onset of multipolar order., Comment: 7 pages, 4 figures

Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand). This problem captures, for example, situations where a merchant and a brand cooperatively bid in an auction to advertise a product, and both benefit from the ad being shown. A mechanism collects bids from the two and decides whether to allocate and which payments the two parties should make. This gives rise to intricate incentive compatibility constraints, e.g., on how to split payments between the two parties. We approach the problem of finding a revenue-maximizing incentive-compatible mechanism from an online learning perspective; this poses significant technical challenges. First, the action space (the class of all possible mechanisms) is huge; second, the function that maps mechanisms to revenue is highly irregular, ruling out standard discretization-based approaches. In the stochastic setting, we design an efficient learning algorithm achieving a regret bound of $O(T^{3/4})$. Our approach is based on an adaptive discretization scheme of the space of mechanisms, as any non-adaptive discretization fails to achieve sublinear regret. In the adversarial setting, we exploit the non-Lipschitzness of the problem to prove a strong negative result, namely that no learning algorithm can achieve more than half of the revenue of the best fixed mechanism in hindsight. We then consider the $\sigma$-smooth adversary; we construct an efficient learning algorithm that achieves a regret bound of $O(T^{2/3})$ and builds on a succinct encoding of exponentially many experts. Finally, we prove that no learning algorithm can achieve less than $\Omega(\sqrt T)$ regret in both the stochastic and the smooth setting, thus narrowing the range where the minimax regret rates for these two problems lie., Comment: Paper accepted at ACM EC 2024

The increasing computing power and bandwidth of programmable digital devices opens new possibilities in the field of real-time processing of HEP data. The LHCb collaboration is exploiting these technology advancements in various ways to enhance its capability for complex data reconstruction in real time. Amongst them is the real-time reconstruction of hits in the VELO pixel detector, by means of real-time cluster-finding embedded in the readout board firmware. This reconstruction, in addition to saving data-acquisition bandwidth and high-level trigger computing resources, also enables further useful applications in precision monitoring and diagnostics of LHC beam conditions. In fact, clusters of pixels, being more reliable and robust indications of physical particle hits than raw pixel counts, are also exempt from the complications associated to the reconstruction of tracks, that involves alignment issues and is sensitive to multi-layer efficiency products. In this paper, we describe the design and implementation of a flexible system embedded in the readout firmware of the VELO detector, allowing real-time measurement of cluster density in several parts of the detector simultaneously, and separately for every bunch ID, for every single LHC collision, without any slowdown of data acquisition. Quantitative applications of this system to luminosity measurement and beam monitoring are demonstrated., Comment: 4 pages, 5 figures, Proceedings of the ACAT 2024 conference

Including the effect of the molecular environment in the numerical modeling of time-resolved electronic spectroscopy remains an important challenge in computational spectroscopy. In this contribution, we present a general approach for the simulation of the optical response of multi-chromophore systems in a structured environment and its implementation as a quantum algorithm. A key step of the procedure is the pseudomode embedding of the system-environment problem resulting in a finite set of quantum states evolving according to a Markovian quantum master equation. This formulation is then solved by a collision model integrated into a quantum algorithm designed to simulate linear and nonlinear response functions. The workflow is validated by simulating spectra for the prototypical excitonic dimer interacting with fast (memoryless) and finite-memory environments. The results demonstrate, on the one hand, the potential of the pseudomode embedding for simulating the dynamical features of nonlinear spectroscopy, including lineshape, spectral diffusion, and relaxations along delay times. On the other hand, the explicit synthesis of quantum circuits provides a fully quantum simulation protocol of nonlinear spectroscopy harnessing the efficient quantum simulation of many-body dynamics promised by the future generation of fault-tolerant quantum computers.

Using school choice as a motivating example, we introduce a stylized model of a many-to-one matching market where the clearinghouse aims to implement contingent priorities, i.e., priorities that depend on the current assignment, to prioritize students with siblings and match them together. We provide a series of guidelines and introduce two natural approaches to implement them: (i) absolute, whereby a prioritized student can displace any student without siblings assigned to the school, and (ii) partial, whereby prioritized students can only displace students that have a less favorable lottery than their priority provider. We study several properties of the corresponding mechanisms, including the existence of a stable assignment under contingent priorities, the complexity of deciding whether there exists one, and its incentive properties. Furthermore, we introduce a soft version of these priorities to guarantee existence, and we provide mathematical programming formulations to find such stable matching or certify that one does not exist. Finally, using data from the Chilean school choice system, we show that our framework can significantly increase the number of students assigned to their top preference and the number of siblings assigned together relative to current practice.

Electron optics deals with the wave-nature of charge carriers to induce, investigate and exploit coherent phenomena in solid state devices, in analogy with optics and photonics. Typically, these goals are achieved in complex electronic devices taking advantage of the macroscopically coherent charge transport in two dimensional electron gases and superconductors. Here, we demonstrate a simple counterintuitive architecture employing intentionally-created lattice defects to induce collective coherent effects in the charge transport of graphene. More specifically, multiple Fabry-P\'erot cavities are produced by irradiating graphene via low-energy electron-beam to form periodically alternated defective and pristine nano-stripes. The enhanced hole-doping in the defective stripes creates potential barriers behaving as partially reflecting mirrors and resonantly confining the carrier-waves within the pristine areas. The interference effects are both theoretically and experimentally investigated and manifest as sheet resistance oscillations up to 30 K for both polarities of charge carriers, contrarily to traditional electrostatically-created Fabry-P\'erot interferometers. Our findings propose defective graphene as an original platform for the realization of innovative coherent electronic devices with applications in nano and quantum technologies., Comment: 29 pages, 4 figures main text, 8 figures supplementary

Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as the softmax of dot products between keys and queries. Recent work has explored alternatives to softmax attention in transformers, such as ReLU and sigmoid activations. In this work, we revisit sigmoid attention and conduct an in-depth theoretical and empirical analysis. Theoretically, we prove that transformers with sigmoid attention are universal function approximators and benefit from improved regularity compared to softmax attention. Through detailed empirical analysis, we identify stabilization of large initial attention norms during the early stages of training as a crucial factor for the successful training of models with sigmoid attention, outperforming prior attempts. We also introduce FLASHSIGMOID, a hardware-aware and memory-efficient implementation of sigmoid attention yielding a 17% inference kernel speed-up over FLASHATTENTION2 on H100 GPUs. Experiments across language, vision, and speech show that properly normalized sigmoid attention matches the strong performance of softmax attention on a wide range of domains and scales, which previous attempts at sigmoid attention were unable to fully achieve. Our work unifies prior art and establishes best practices for sigmoid attention as a drop-in softmax replacement in transformers.