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The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecology and wildlife research. The use of camera traps offers the opportunity to quickly collect large quantities of photographs that capture wildlife in its natural habitat, avoiding factors that could alter their behavior. In Tierra del Fuego, Argentina, research is being conducted on forest use by different herbivores (guanacos, cows, sheep) to optimize management and protect these natural ecosystems. Although camera traps allow for the collection of millions of images, interpreting such photographs presents a scalability challenge for manual processing. As a result, much of the valuable knowledge stored in these vast data repositories remains untapped. Neural Networks and Deep Learning are areas of study within Artificial Intelligence. Over the past decade, these two disciplines have made significant contributions to image recognition on a global scale. Ecological and wildlife conservation studies can be combined with these new technologies to extract important information from the photographs obtained by camera traps, contributing to the understanding of various natural processes and improving the management of the involved wild areas. Our project aims to develop neural network models to classify animal species in photographs taken with camera traps, addressing large-scale challenges in scientific research., Comment: in Spanish language, XXIV Workshop de Investigadores en Ciencias de la Computaci\'on (WICC 2022, Mendoza)

This paper presents a novel deep learning-based approach named RealDiffFusionNet incorporating Neural Controlled Differential Equations (Neural CDE) - time series models that are robust in handling irregularly sampled data - and multi-head attention to align relevant multimodal context (image data, time invariant data, etc.) at each time point. Long short-term memory (LSTM) models were also used as a baseline. Two different datasets were used: a data from the Open-Source Imaging Consortium (OSIC) containing structured time series data of demographics and lung function with a baseline CT scan of the lungs and the second from the Alzheimer's Disease Neuroimaging Initiative (ADNI) containing a series of MRI scans along with demographics, physical examinations, and cognitive assessment data. An ablation study was performed to understand the role of CDEs, multimodal data, attention fusion, and interpolation strategies on model performance. When the baseline models were evaluated, the use of multimodal data resulted in an improvement in Neural CDE performance, with a lower test RMSE. Additionally, the performance of multimodal Neural CDE was also superior to multimodal LSTM. In the attention-based architectures, fusion through concatenation and rectilinear interpolation were found to improve model performance. The performance of the proposed RealDiffFusionNet was found to be superior (0.2570) to all models. For the ADNI dataset, between the Neural-CDE and LSTM models trained only on the structured data, the test RMSE were comparable (0.471 for LSTM vs. 0.4581 Neural-CDE). Furthermore, the addition of image features from patients' MRI series resulted in an improvement in performance, with a lower test RMSE (0.4372 with multimodal vs 0.4581 with structured data). RealDiffFusionNet has shown promise in utilizing CDEs and multimodal data to accurately predict disease progression.

We discuss $(K,N)$-convexity and gradient flows for $(K,N)$-convex functionals on metric spaces, in the case of real $K$ and negative $N$. In this generality, it is necessary to consider functionals unbounded from below and/or above, possibly attaining as values both the positive and the negative infinity. We prove several properties of gradient flows of $(K,N)$-convex functionals characterized by Evolution Variational Inequalities, including contractivity, regularity, and uniqueness., Comment: 23 pages

Measuring fluctuations in matter's low energy excitations is the key to unveil the nature of the nonequilibrium response of materials. A promising outlook in this respect is offered by spectroscopic methods that address matter fluctuations by exploiting the statistical nature of light-matter interactions with weak few-photon probes. Here we report the first implementation of ultrafast phase randomized tomography, combining pump-probe experiments with quantum optical state tomography, to measure the ultrafast non-equilibrium dynamics in complex materials. Our approach utilizes a time-resolved multimode heterodyne detection scheme with phase-randomized coherent ultrashort laser pulses, overcoming the limitations of phase-stable configurations and enabling a robust reconstruction of the statistical distribution of phase-averaged optical observables. This methodology is validated by measuring the coherent phonon response in $\alpha$-quartz. By tracking the dynamics of the shot-noise limited photon number distribution of few-photon probes with ultrafast resolution, our results set an upper limit to the non-classical features of phononic state in $\alpha$-quartz and provide a pathway to access nonequilibrium quantum fluctuations in more complex quantum materials., Comment: 21 pages, 10 figures (including Supplementary Information)

The use of optical cavities on resonance with material excitations allows controlling light-matter interaction in both the regimes of weak and strong coupling. We study here the multimode vibrational coupling of low energy phonons in the charge-density-wave material 1T-TaS$_{2}$ across its insulator-to-metal phase transition. For this purpose, we embed 1T-TaS$_{2}$ into THz Fabry-P\'erot cryogenic cavities tunable in frequency within the spectral range of the vibrational modes of the insulating phase and track the linear response of the coupled phonons across the insulator-to-metal transition. In the low temperature dielectric state, we reveal the signatures of a multimode vibrational strong collective coupling. The observed polariton modes inherit character from all the vibrational resonances as a consequence of the cavity-mediated hybridization. We reveal that the vibrational strong collective coupling is suppressed across the insulator-to-metal transition as a consequence of the phonon-screening induced by the free charges. Our findings emphasize how the response of cavity-coupled vibrations can be modified by the presence of free charges, uncovering a new direction toward the tuning of coherent light-matter interaction in cavity-confined correlated materials., Comment: 24 pages, 9 figures (including Supplementary Material)

There has been substantial growth in the UAV market along with an expansion in their applications. However, the successful execution of a UAV mission is very often dependent on the use of a GNSS. Unfortunately, the vulnerability of GNSS signals, due to their lack of encryption and authentication, poses a significant cybersecurity issue. This vulnerability makes various attacks, particularly the "GNSS spoofing attack," and "GNSS jamming attack" easily executable. Generally speaking, during this attack, the drone is manipulated into altering its path, usually resulting in an immediate forced landing or crash. As far as we know, we are the first to propose a lightweight-solution that enable a drone to autonomously rescue itself, assuming it is under GNSS attack and the GNSS is no longer available, and return safely to its initial takeoff position, thereby preventing any potential crashes. During the flight, wind plays a critical role as it can instantaneously alter the drone's position. To solve this problem, we have devised a highly effective 2-phases solution: (i) Forward Phase, for monitoring and recording the forward journey, and (ii) Backward Phase, that generates a backward route, based on the Forward Phase and wind presence. The final solution ensures strong performance in consistently returning the drone to the original position, even in wind situations, while maintaining a very fast computation time., Comment: This article has been accepted for publication in the proceedings of the 2024 IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

We study the convergence of an $N$-particle Markovian controlled system to the solution of a family of stochastic McKean-Vlasov control problems, either with a finite horizon or Schr\"odinger type cost functional. Specifically, under suitable assumptions, we prove the convergence of the value functions, the fixed-time probability distributions, and the relative entropy of their path-space probability laws. These proofs are based on a Benamou-Brenier type reformulation of the problem and a superposition principle, both of which are tools from the theory of optimal transport., Comment: 54 pages. Comments are welcome!

The analysis of complex high-dimensional data is a common task in many domains, resulting in bespoke visual exploration tools. Expectations and practices of domain experts as users do not always align with visualization theory. In this paper, we report on a design study in the medical domain where we developed two high-fidelity prototypes encoding EEG-derived brain network data with different types of visualizations. We evaluate these prototypes regarding effectiveness, efficiency, and preference with two groups: participants with domain knowledge (domain experts in medical research) and those without domain knowledge, both groups having little or no visualization experience. A requirement analysis and study of low-fidelity prototypes revealed a strong preference for a novel and aesthetically pleasing visualization design, as opposed to a design that is considered more optimal based on visualization theory. Our study highlights the pros and cons of both approaches, discussing trade-offs between task-specific measurements and subjective preference. While the aesthetically pleasing and novel low-fidelity prototype was favored, the results of our evaluation show that, in most cases, this was not reflected in participants' performance or subjective preference for the high-fidelity prototypes., Comment: 13 pages, 4 figures, 2 tables

Inspired by emergent behaviors of living matter, there is increasing interest in developing approaches to create dynamic patterns and structures in synthetic materials with controllable complexity to enable functionalities that are not possible in thermodynamic equilibrium. Here we show that electrophoretically driven and maintained non-equilibrium gradients of magnetic nanoparticles in non-polar solvent can undergo electrokinetic instabilities (EKI), leading to various electrically controllable spatiotemporally patterned states. These electrokinetic instabilities and patterns can be tuned with a magnetic field via magnetostatic energy reduction mechanism to both increase and decrease the pattern complexity. We reflect the experimental observations on the theoretical electrokinetic and magnetostatic arguments. We further show that small amounts of polar water in the otherwise non-polar system are critical enablers for the electrophoretic mobility of the nanoparticles. Since functionalities of magnetic nanoparticles are widely tunable, we foresee that the combination of dissipative electrokinetic driving and magnetic energy reduction can lead to novel functional dissipative materials.

Multi-tasking machine learning (ML) models exhibit prediction abilities in domains with little to no training data available (few-shot and zero-shot learning). Over-parameterized ML models are further capable of zero-loss training and near-optimal generalization performance. An open research question is, how these novel paradigms contribute to solving tasks related to enhancing the renewable energy transition and mitigating climate change. A collection of unified ML tasks and datasets from this domain can largely facilitate the development and empirical testing of such models, but is currently missing. Here, we introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets from six different application domains related to enhancing renewable energy, including out-of-distribution validation and testing data. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model. We further analyse the dimensions of each dataset; investigate what they require for designing over-parameterized models; introduce a set of dataset scores that describe important properties of each task and dataset; and provide performance benchmarks.

We establish a nonlinear analogue of a splitting map into a Euclidean space, as a harmonic map into a flat torus. We prove that the existence of such a map implies Gromov-Hausdorff closeness to a flat torus in any dimension. Furthermore, Gromov-Hausdorff closeness to a flat torus and an integral bound {on $r_M(x)$, the smallest eigenvalue of the Ricci tensor $\text{ric}_x$ in $x$}, imply the existence of a harmonic splitting map. Combining these results with Stern's inequality, we provide a new Gromov-Hausdorff stability theorem for flat $3$-tori. The main tools we employ include the harmonic map heat flow, Ricci flow, and both Ricci limits and RCD theories., Comment: 44 pages. We corrected a mistake in Theorem 5.3, present in the previous version, and added Theorem 1.8

In the cuprates, the opening of a d-wave superconducting (SC) gap is accompanied by a redistribution of spectral weight at energies two orders of magnitude larger than this gap. This indicates the importance to the pairing mechanism of on-site electronic excitations, such as orbital transitions or charge transfer excitations. Here, we resort to a three-pulse pump-probe scheme to study the broadband non-equilibrium dielectric function in optimally-doped $Bi_2Sr_2Ca_{0.92}Y_{0.08}Cu_2O_{8+{\delta}}$ and we identify two distinct dynamical responses: i) a blueshift of the central energy of an interband excitation peaked at 2 eV and ii) a change in spectral weight in the same energy range. Photoexcitation with near-IR and mid-IR pulses, with photon energies respectively above and below the SC gap, reveals that the transient changes in the central energy are not modified by the onset of superconductivity and do not depend on the pump photon energy. Conversely, the spectral weight dynamics strongly depends on the pump photon energy and has a discontinuity at the critical temperature. The picture that emerges is that, while high-energy pulses excite quasiparticles in both nodal and thermally inaccessible antinodal states, photoexcitation by low-energy pulses mostly accelerates the condensate and creates excitations predominantly at the nodes of the SC gap. These results, rationalized by kinetic equations for d-wave superconducting gaps, indicate that dynamical control of the momentum-dependent distribution of non-thermal quasiparticles may be achieved by the selective tuning of the photoexcitation.

For negative ion beam sources there are several methods of measuring the accelerated beam current, most commonly electrical measurements at the power supply and calorimetric measurements. On SPIDER, the ITER Heating Neutral Beam full-scale beam source prototype, electrical measurements at the acceleration grid power supply (AGPS) are complemented by polarizing the diagnostic calorimeter STRIKE to provide an additional electrical measurement of the accelerated current. This is in addition to the calorimetric measurements provided by STRIKE. These diagnostics give differing measurements of the beam current. Exploiting the reduced number of open apertures on SPIDER a new beam diagnostic has been installed to measure the individual beamlet currents directly. The so called Beamlet Current Monitor (BCM) has been used to measure the current of five beamlets during the most recent SPIDER campaign. This work compares the BCM current to the electrical measurements at the AGPS and STRIKE. The average BCM current agrees well with the STRIKE electrical measurements, indicating that the AGPS overestimates the beam current. The individual beamlets are compared to the STRIKE calorimetric measurements, showing similar current trends with the source parameters.

Identifying molecules that exhibit some pre-specified properties is a difficult problem to solve. In the last few years, deep generative models have been used for molecule generation. Deep Graph Variational Autoencoders are among the most powerful machine learning tools with which it is possible to address this problem. However, existing methods struggle in capturing the true data distribution and tend to be computationally expensive. In this work, we propose RGCVAE, an efficient and effective Graph Variational Autoencoder based on: (i) an encoding network exploiting a new powerful Relational Graph Isomorphism Network; (ii) a novel probabilistic decoding component. Compared to several state-of-the-art VAE methods on two widely adopted datasets, RGCVAE shows state-of-the-art molecule generation performance while being significantly faster to train.

Using only image-sentence pairs, weakly-supervised visual-textual grounding aims to learn region-phrase correspondences of the respective entity mentions. Compared to the supervised approach, learning is more difficult since bounding boxes and textual phrases correspondences are unavailable. In light of this, we propose the Semantic Prior Refinement Model (SPRM), whose predictions are obtained by combining the output of two main modules. The first untrained module aims to return a rough alignment between textual phrases and bounding boxes. The second trained module is composed of two sub-components that refine the rough alignment to improve the accuracy of the final phrase-bounding box alignments. The model is trained to maximize the multimodal similarity between an image and a sentence, while minimizing the multimodal similarity of the same sentence and a new unrelated image, carefully selected to help the most during training. Our approach shows state-of-the-art results on two popular datasets, Flickr30k Entities and ReferIt, shining especially on ReferIt with a 9.6% absolute improvement. Moreover, thanks to the untrained component, it reaches competitive performances just using a small fraction of training examples.

SPIDER is the full-scale prototype of the ion source of the ITER Heating Neutral Beam Injector, where negative ions of Hydrogen or Deuterium are produced by a RF generated plasma and accelerated with a set of grids up to ~100 keV. The Power Supply System is composed of high voltage dc power supplies capable of handling frequent grid breakdowns, high current dc generators for the magnetic filter field and RF generators for the plasma generation. During the first 3 years of SPIDER operation different electrical issues were discovered, understood and addressed thanks to deep analyses of the experimental results supported by modelling activities. The paper gives an overview on the observed phenomena and relevant analyses to understand them, on the effectiveness of the short-term modifications provided to SPIDER to face the encountered issues and on the design principle of long-term solutions to be introduced during the currently ongoing long shutdown., Comment: 8 pages, 12 figures. Presented at SOFT 2022

ITER envisages the use of two heating neutral beam injectors plus an optional one as part of the auxiliary heating and current drive system. The 16.5 MW expected neutral beam power per injector is several notches higher than worldwide existing facilities. A Neutral Beam Test Facility (NBTF) was established at Consorzio RFX, exploiting the synergy of two test beds, SPIDER and MITICA. SPIDER is dedicated to developing and characterizing large efficient negative ion sources at relevant parameters in ITER-like conditions: source and accelerator located in the same vacuum where the beam propagates, immunity to electromagnetic interferences of multiple radio-frequency (RF) antennas, avoidance of RF-induced discharges on the outside of the source. Three years of experiments on SPIDER have addressed to the necessary design modifications to enable full performances. The source is presently under a long shut-down phase to incorporate learnings from the experimental campaign. Parallelly, developments on MITICA, the full-scale prototype of the ITER NBI featuring a 1 MV accelerator and ion neutralization, are underway including manufacturing of in-vessel components, while power supplies and auxiliary plants are already under final testing and commissioning. Integration, commissioning and tests of the 1MV power supplies are essential for this first-of-kind system, unparalleled both in research and industry field. The integrated test to confirm 1MV output by combining invertor systems, DC generators and transmission lines extracted errors/accidents in some components. To realize a concrete system for ITER, solutions for the repair and the improvement of the system were developed. Hence, NBTF is emerging as a necessary facility, due to the large gap with existing injectors, effectively dedicated to identify issues and find solutions to enable successful ITER NBI operations in a time bound fashion.

Placing quantum materials into optical cavities provides a unique platform for controlling quantum cooperative properties of matter, via both weak and strong light-matter coupling. Here we report the experimental evidence of reversible cavity control of a metal-to-insulator phase transition in a correlated solid-state material. We embed the charge density wave material 1T-TaS$_{2}$ into cryogenic tunable terahertz cavities and show that a switch between conductive and insulating behaviors, associated with a large change in the sample temperature, is obtained by mechanically tuning the distance between the cavity mirrors and their alignment. The large thermal modification observed is indicative of a Purcell-like scenario in which the spectral profile of the cavity modifies the energy exchange between the material and the external electromagnetic field. Our findings provide opportunities for controlling the thermodynamics and macroscopic transport properties of quantum materials by engineering their electromagnetic environment., Comment: 52 pages, 36 figures (including Supplementary Information)

The aim of this paper is to show the existence of a canonical distance $\mathsf d'$ defined on a locally Minkowski metric measure space $(\mathsf X,\mathsf d,\mathfrak m)$ such that: i) $\mathsf d'$ is equivalent to $\mathsf d$, ii) $(\mathsf X, \mathsf d', \mathfrak m)$ is infinitesimally Hilbertian. This new regularity assumption on $(\mathsf X, \mathsf d,\mathfrak m)$ essentially forces the structure to be locally similar to a Minkowski space and defines a class of metric measure structures which includes all the Finsler manifolds, and it is actually strictly larger. The required distance $\mathsf d'$ will be the intrinsic distance $\mathsf d_\mathsf{KS}$ associated to the so-called Korevaar-Schoen energy, which is proven to be a quadratic form. In particular, we show that the Cheeger energy associated to the metric measure space $(\mathsf X, \mathsf d_\mathsf{KS}, \mathfrak m)$ is in fact the Korevaar-Schoen energy.

We report here the realization and commissioning of an experiment dedicated to the study of the optical properties of light matter hybrids constituted of crystalline samples embedded in an optical cavity. The experimental assembly developed offers the unique opportunity to study the weak and strong coupling regime between a tunable optical cavity in cryogenic environment and low energy degrees of freedom such as phonons, magnons or charge fluctuations. We describe here the setup developed which allows the positioning of crystalline samples in an optical cavity of different quality factor, the tuning of the cavity length at cryogenic temperatures and its optical characterization with a broadband time domain THz spectrometer (0.2-6 THz). We demonstrate the versatility of the setup by studying the vibrational strong coupling in CuGeO3 single crystal at cryogenic temperatures.

Given any $d$-dimensional Lipschitz Riemannian manifold $(M,g)$ with heat kernel $\mathsf{p}$, we establish uniform upper bounds on $\mathsf{p}$ which can always be decoupled in space and time. More precisely, we prove the existence of a constant $C>0$ and a bounded Lipschitz function $R\colon M \to (0,\infty)$ such that for every $x\in M$ and every $t>0$, \begin{align*} \sup_{y\in M} \mathsf{p}(t,x,y) \leq C\min\{t, R^2(x)\}^{-d/2}. \end{align*} This allows us to identify suitable weighted Lebesgue spaces w.r.t. the given volume measure as subsets of the Kato class induced by $(M,g)$. In the case $\partial M \neq \emptyset$, we also provide an analogous inclusion for Lebesgue spaces w.r.t. the surface measure on $\partial M$. We use these insights to give sufficient conditions for a possibly noncomplete Lipschitz Riemannian manifold to be tamed, i.e. to admit a measure-valued lower bound on the Ricci curvature, formulated in a synthetic sense., Comment: 28 pages. Comments welcome

The competition between the electron-hole Coulomb attraction and the three-dimensional dielectric screening dictates the optical properties of layered semiconductors. In low-dimensional materials, the equilibrium dielectric environment can be significantly altered by the ultrafast excitation of photo-carriers, leading to renormalized band gap and exciton binding energies. Recently, black phosphorus emerged as a 2D material with strongly layer-dependent electronic properties. Here, we resolve the coherent response of screening to sub-gap photo-excitation in bulk black phosphorus and find that mid-infrared pulses tuned across the band gap drive a transient non-thermal suppression of the dielectric screening, which is revealed by the emergence of the single-layer exciton resonance. Our work exposes the role of interlayer interactions in determining the electronic properties of 2D materials and discloses the possibility of optically manipulate them, which is of great relevance for the engineering of versatile van der Waals low-dimensional materials.

Given a textual phrase and an image, the visual grounding problem is the task of locating the content of the image referenced by the sentence. It is a challenging task that has several real-world applications in human-computer interaction, image-text reference resolution, and video-text reference resolution. In the last years, several works have addressed this problem by proposing more and more large and complex models that try to capture visual-textual dependencies better than before. These models are typically constituted by two main components that focus on how to learn useful multi-modal features for grounding and how to improve the predicted bounding box of the visual mention, respectively. Finding the right learning balance between these two sub-tasks is not easy, and the current models are not necessarily optimal with respect to this issue. In this work, we propose a loss function based on bounding boxes classes probabilities that: (i) improves the bounding boxes selection; (ii) improves the bounding boxes coordinates prediction. Our model, although using a simple multi-modal feature fusion component, is able to achieve a higher accuracy than state-of-the-art models on two widely adopted datasets, reaching a better learning balance between the two sub-tasks mentioned above.

In this paper we investigate two important properties of metric measure spaces satisfying the reduced curvature-dimension condition for negative values of the dimension parameter: the existence of a transport map between two suitable marginals and the so-called local-to-global property.

The MIP Timing Detector will provide additional timing capabilities for detection of minimum ionizing particles (MIPs) at CMS during the High Luminosity LHC era, improving event reconstruction and pileup rejection. The central portion of the detector, the Barrel Timing Layer (BTL), will be instrumented with LYSO:Ce crystals and Silicon Photomultipliers (SiPMs) providing a time resolution of about 30 ps at the beginning of operation, and degrading to 50-60 ps at the end of the detector lifetime as a result of radiation damage. In this work, we present the results obtained using a 120 GeV proton beam at the Fermilab Test Beam Facility to measure the time resolution of unirradiated sensors. A proof-of-concept of the sensor layout proposed for the barrel region of the MTD, consisting of elongated crystal bars with dimensions of about 3 x 3 x 57 mm$^3$ and with double-ended SiPM readout, is demonstrated. This design provides a robust time measurement independent of the impact point of the MIP along the crystal bar. We tested LYSO:Ce bars of different thickness (2, 3, 4 mm) with a geometry close to the reference design and coupled to SiPMs manufactured by Hamamatsu and Fondazione Bruno Kessler. The various aspects influencing the timing performance such as the crystal thickness, properties of the SiPMs (e.g. photon detection efficiency), and impact angle of the MIP are studied. A time resolution of about 28 ps is measured for MIPs crossing a 3 mm thick crystal bar, corresponding to an MPV energy deposition of 2.6 MeV, and of 22 ps for the 4.2 MeV MPV energy deposition expected in the BTL, matching the detector performance target for unirradiated devices.

We study the problem of whether the curvature-dimension condition with negative values of the generalized dimension parameter is stable under a suitable notion of convergence. To this purpose, first of all we introduce an appropriate setting to introduce the CD(K, N)-condition for $N < 0$, allowing metric measure structures in which the reference measure is quasi-Radon. Then in this class of spaces we introduce the distance $d_{\mathsf{iKRW}}$, which extends the already existing notions of distance between metric measure spaces. Finally, we prove that if a sequence of metric measure spaces satisfying the CD(K, N)-condition with $N < 0$ is converging with respect to the distance $d_{\mathsf{iKRW}}$ to some metric measure space, then this limit structure is still a CD(K, N) space.

Recently, Unmanned Aerial Vehicles (UAVs) are employed for a plethora of civilian applications. Such flying vehicles can accomplish tasks under the pilot's eyesight within the range of a remote controller, or autonomously according to a certain pre-loaded path configuration. Different path deviation attacks can be performed by malicious users against UAVs. We classify such attacks and the relative defenses based on the UAV's flight mode, i.e., (i) First Person View (FPV), (ii) civilian Global Navigation Satellite System based (GNSS), and (iii) GNSS "plus" auxiliary technologies (GNSS+), and on the multiplicity, i.e., (i) Single UAV, and (ii) Multiple UAVs. We found that very little has been done to secure the FPV flight mode against path deviation. In GNSS mode, spoofing is the most worrisome attack. The best defense against spoofing seems to be redundancy, such as adding vision chips to single UAV or using multiple arranged UAVs. No specific attacks and defenses have been found in literature for GNSS+ or for UAVs moving in group without a pre-ordered arrangement. These aspects require further investigation., Comment: Published in: 2020 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops)

In this work, the problem of localizing ground devices (GDs) is studied comparing the performance of four range-free (RF) localization algorithms that use a mobile anchor (MA). All the investigated algorithms are based on the so-called heard/not-heard (HnH) method, which allows the GDs to detect the MA at the border of their antenna communication radius. Despite the simplicity of this method, its efficacy in terms of accuracy is poor because it relies on the antenna radius that continuously varies under different conditions. Usually, the antenna radius declared by the manufacturer does not fully characterize the actual antenna radiation pattern. In this paper, the radiation pattern of the commercial DecaWave DWM1001 Ultra-Wide-Band (UWB) antennas is observed in a real test-bed at different altitudes for collecting more information and insights on the antenna radius. The compared algorithms are then tested using both the observed and the manufacturer radii. The experimental accuracy is close to the expected theoretical one only when the antenna pattern is actually omnidirectional. However, typical antennas have strong pattern irregularities that decrease the accuracy. For improving the performance, we propose range-based (RB) variants of the compared algorithms in which, instead of using the observed or the manufacturer radii, the actual measured distances between the MA and the GD are used. The localization accuracy tremendously improves confirming that the knowledge of the exact antenna pattern is essential for any RF algorithm., Comment: unofficially accepted version (Elsevier Pervasive and Mobile Computing, PMC)

We study partial derivatives on the product of two metric measure structures, in particular in connection with calculus via modules as proposed by the first named author. Our main results are 1) The extension to this non-smooth framework of Schwarz's theorem about symmetry of mixed second derivatives, 2) a quite complete set of results relating the property $f\in W^{2,2}(\X\times\Y)$ on one side with that of $f(\cdot,y)\in W^{2,2}(\X)$ and $f(x,\cdot)\in W^{2,2}(\Y)$ for a.e.\ $y,x$ respectively on the other. Here $\X,\Y$ are $\RCD$ spaces so that second order Sobolev spaces are well defined. \end{itemize} These results are in turn based upon the study of Sobolev regularity, and of the underlying notion of differential, for a map with values in a Hilbert module: we mainly apply this notion to the map $x\mapsto\d_\sy f(x,\cdot)$ in order to build, under the appropriate regularity requirements, its differential $\d_\sx\d_\sy f$.

It is well-known that the flows generated by two smooth vector fields commute, if the Lie bracket of these vector fields vanishes. This assertion is known to extend to Lipschitz continuous vector fields, up to interpreting the vanishing of their Lie bracket in the sense of almost everywhere equality. We show that this cannot be extended to general a.e. differentiable vector fields admitting a.e. unique flows. We show however that the extension holds when one field is Lipschitz continuous and the other one is merely Sobolev regular (but admitting a regular Lagrangian flow).

Recent advances in acquisition equipment is providing experiments with growing amounts of precise yet affordable sensors. At the same time an improved computational power, coming from new hardware resources (GPU, FPGA, ACAP), has been made available at relatively low costs. This led us to explore the possibility of completely renewing the chain of acquisition for a fusion experiment, where many high-rate sources of data, coming from different diagnostics, can be combined in a wide framework of algorithms. If on one hand adding new data sources with different diagnostics enriches our knowledge about physical aspects, on the other hand the dimensions of the overall model grow, making relations among variables more and more opaque. A new approach for the integration of such heterogeneous diagnostics, based on composition of deep variational autoencoders, could ease this problem, acting as a structural sparse regularizer. This has been applied to RFX-mod experiment data, integrating the soft X-ray linear images of plasma temperature with the magnetic state. However to ensure a real-time signal analysis, those algorithmic techniques must be adapted to run in well suited hardware. In particular it is shown that, attempting a quantization of neurons transfer functions, such models can be modified to create an embedded firmware. This firmware, approximating the deep inference model to a set of simple operations, fits well with the simple logic units that are largely abundant in FPGAs. This is the key factor that permits the use of affordable hardware with complex deep neural topology and operates them in real-time., Comment: Proceeding of the 22nd IEEE Real Time Conference

Ferrofluids are strongly magnetic fluids consisting of magnetic nanoparticles dispersed in a carrier fluid. Besides their technological applications, they have a tendency to form beautiful and intriguing patterns when subjected to external static and dynamic magnetic fields. Most of the patterns occur in systems consisting of two fluids: one ferrofluidic and one non-magnetic (oil, air, etc.), wherein the fluid-fluid interface deforms as a response to magnetic fields. Usually, the fluids are completely immiscible and so the interfacial energy in this systems is very large. Here we show that it is possible to design a fully aqueous ferrofluid system by using phase separation of incompatible polymers. This continuous aqueous system allows an ultralow interfacial tension (down to 1 $\mu$N/m) and nearly vanishing pinning at three phase contact lines. We demonstrate the normal-field instability with the system and focus on the miniaturization of the pattern length from the typical $\sim$10 mm size down to $\sim$200 $\mu$m. The normal-field instability is characterized in glass capillaries of thickness comparable to the pattern length. This system paves way towards interesting physics such as the interaction between magnetic instabilities and thermal capillary waves and offers a way to evaluate extremely small interfacial tensions.

Materials with continuous dissipation can exhibit responses and functionalities that are not possible in thermodynamic equilibrium. While this concept is well-known, a major challenge has been the implementation: how to rationally design materials with functional non-equilibrium states and quantify the dissipation? Here we address these questions for the widely used colloidal nanoparticles that convey several functionalities. We propose that useful non-equilibrium states can be realised by creating and maintaining steady-state nanoparticle concentration gradients by continuous injection and dissipation of energy. We experimentally demonstrate this with superparamagnetic iron oxide nanoparticles that in thermodynamic equilibrium form a homogeneous functional fluid with a strong magnetic response (a ferrofluid). To create non-equilibrium functionalities, we charge the nanoparticles with anionic charge control agents to create electroferrofluids where nanoparticles act as charge carriers that can be driven with electric fields and current to non-homogeneous dissipative steady-states. The dissipative steady-states exhibit voltage-controlled magnetic properties and emergent diffuse interfaces. The diffuse interfaces respond strongly to external magnetic fields, leading to dissipative patterns that are not possible in the equilibrium state. We identify the closest non-dissipative analogues of these dissipative patterns, discuss the differences, and highlight how pattern formation in electroferrofluids is linked to dissipation that can be directly quantified. Beyond electrically controlled ferrofluids and patterns, we foresee that the concept can be generalized to other functional nanoparticles to create various scientifically and technologically relevant non-equilibrium states with optical, electrical, catalytic, and mechanical responses that are not possible in thermodynamic equilibrium.

We develop the theory of tamed spaces which are Dirichlet spaces with distribution-valued lower bounds on the Ricci curvature and investigate these from an Eulerian point of view. To this end we analyze in detail singular perturbations of Dirichlet form by a broad class of distributions. The distributional Ricci bound is then formulated in terms of an integrated version of the Bochner inequality using the perturbed energy form and generalizing the well-known Bakry-\'Emery curvature-dimension condition. Among other things we show the equivalence of distributional Ricci bounds to gradient estimates for the heat semigroup in terms of the Feynman-Kac semigroup induced by the taming distribution as well as consequences in terms of functional inequalities. We give many examples of tamed spaces including in particular Riemannian manifolds with either interior singularities or singular boundary behavior., Comment: 68 pages; comments welcome!

In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the complex laws governing the chemical world. In this work, we explore the usage of the histogram of atom valences to drive the generation of molecules in such models. We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model, and shows improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.

In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.

Unveiling and controlling the coherent evolution of low energy states is a key to attain light driven new functionalities of materials. Here we investigate the coherent evolution of non-equilibrium photon-phonon Raman interactions in the low photon number regime via femtosecond time-resolved multimode heterodyne detection. The time dependence of the weak probe spectral components is revealed by interferential amplification with shaped phase-locked reference fields that allow for a frequency selective amplification. We report measurements on $\alpha$-quartz that show that both amplitude and phase of the probe spectral components are modulated at the phonon frequency, but encode qualitatively different responses which are representative respectively of the time dependent position and momentum of the atoms. We stress that the sensitivity achieved here (1-10 photons per pulse) may be of relevance for both quantum information technologies and time domain studies on photosensitive materials.

The lack of comprehensive monitoring equipment in low voltage (LV) residential feeders, impedes a near-term deployment of centralized schemes for the integration of domestic-scale distributed generation (DG). In this context, this paper introduces a technique that generates a set of fitted polynomials, derived from offline simulations and regression analysis, that characterise the magnitude of representative network variables (i.e. key for network operation) as a direct analytical expression of the controllable local conditions of any DG unit (i.e. active and reactive power injections). Crucially, the coefficients of these polynomials can be estimated, autonomously at the location of each DG unit, without the need for remote monitoring (i.e. using only locally available measurements). During online implementation, the method consists only of direct calculations (i.e. non-iterative), facilitating real-time operation. The accuracy of the polynomials to estimate the magnitude of the network variables is assessed under multiple scenarios on a representative radial LV feeder. Furthermore, the robustness of the method is demonstrated under the presence of new generation and electric vehicles.

The RFX-mod2 Nuclear Fusion experiment is an upgrade of RFX-mod, shutdown in 2016. Among the other improvements in the machine structure and diagnostics, a larger number of electromagnetic probes (EMs) is foreseen to provide more information about plasma instabilities and to allow an improved real-time plasma control. An Analog to Digital Converter (ADC) architecture able to provide at the same time both transient recording and real-time streaming, as well as FPGA-based time integration of the inputs is foreseen in RFX-mod2. Transient recording provides full speed data acquisition (up to 1 MSample/s) by recording data in local memory and reading memory content after the plasma discharge. Real-time streaming of sub-sampled data is required for active control. The chosen technology is based on the XILINX Zynq architecture that provides in the same chip a multi-core ARM processor tightly coupled to a FPGA. Time critical functions are carried out by the FPGA, such as the management of the circular data buffer, low pass filtering for subsampling of the samples to be streamed and digital integration. Other functions are carried out by the processor, such as the management of the configuration setting, received via TCP/IP or HTTP, the data readout of acquired samples in transient recording buffers and network data streaming of data collected for active real-time plasma control.

The characterization of laboratory plasma instabilities, magnetic reconnection and turbulence associated phenomena, require the simultaneous signal sampling from arrays of magnetic sensors (hundreds or even thousands) to obtain spatial resolution, with several hundred kHz for time resolution. Magnetic measurements based on pick-up (Mirnov) coil are quite common in experimental pulsed devices for plasma research, thanks to their simplicity and reliability. Being the signal from this type of sensor proportional to the time variation of the magnetic field ($dB/dt$), it has to be time-integrated to recover the instant value of magnetic field. Depending on the required integration time usually either analog integrators or chopped integrators are used. However these solutions tend to limit the frequency bandwidth in the kHz range, they are not easy to design and build, and require additional fast channels to directly acquire the $dB/dt$ signal to recover the plasma dynamic features at higher frequencies. In this paper we evaluate the feasibility of using a direct single channel precision ADC to allow the simultaneous acquisition of $dB/dt$ measurements and to provide the integrated B measurement by means of digital integration on the new RFX-mod2 device. On this purpose we interfaced an existing ADC-module to a Xilinx Zynq FPGA, in order to evaluate the intrinsic noise and to investigate the feasible integration window of this configuration. The result opens the door to a compact, cost-effective and reliable acquisition system, usable for simultaneous real-time control and transient signal recording, scalable from tenths to thousands channels, applicable to a broad class of pulsed plasma experimental devices.

We give a quick and direct proof of the strong maximum principle on finite dimensional $RCD$ spaces based on the Laplacian comparison of the squared distance.

We prove that if the dimension of the first cohomology group of a $RCD^*(0,N)$ space is $N$, then the space is a flat torus. This generalizes a classical result due to Bochner to the non-smooth setting and also provides a first example where the study of the cohomology groups in such synthetic framework leads to geometric consequences.

We demonstrate that muon tomography can be used to precisely measure the properties of various materials. The materials which have been considered have been extracted from an experimental blast furnace, including carbon (coke) and iron oxides, for which measurements of the linear scattering density relative to the mass density have been performed with an absolute precision of 10%. We report the procedures that are used in order to obtain such precision, and a discussion is presented to address the expected performance of the technique when applied to heavier materials. The results we obtain do not depend on the specific type of material considered and therefore they can be extended to any application., Comment: 16 pages, 4 figures

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The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented.

We compare properties of galaxies in loose groups with those in field environment by analyzing the Nearby Optical Galaxy (NOG) catalog of galaxy systems. We consider as group galaxies, objects belonging to systems with at least five members identified by means of the "friends of friends method", and, as field galaxies, all galaxies with no companions. We analyze both a magnitude--limited sample of 959 and 2035 galaxies (groups vs. field galaxies, respectively, B<14 mag, and 2000

We study morphology and luminosity segregation of galaxies in loose groups. We analyze the two catalogs of groups which have been identified in the Nearby Optical Galaxy (NOG) sample, by means of hierarchical and percolation ``friends-of-friends'' methods (HG and PG catalogs, respectively). In the first part of our analysis we consider 387 and 436 groups of HG and PG, respectively, and compare morphology- (luminosity-) weighted to unweighted group properties: velocity dispersion, mean pairwise distance, and mean groupcentric distance of member galaxies. The second part of our analysis is based on two ensemble systems, one for each catalog, built by suitably combining together galaxies of all groups (1584 and 1882 galaxies for HG and PG groups, respectively). We find that earlier-type (brighter) galaxies are more clustered and lie closer to the group centers, both in position and in velocity, than later-type (fainter) galaxies. Spatial segregations are stronger than kinematical segregations. These effects are generally detected at the larger than 3-sigma level. Luminosity segregation is shown to be independent of morphology segregation. Our main conclusions are strengthened by the detection of segregation in both hierarchical and percolation catalogs. Our results agree with a continuum of segregation properties of galaxies in systems, from low-mass groups to massive clusters., Comment: 12 pages, 8 eps figures, submitted for publication in AA

The notion of tamed Dirichlet space was proposed by Erbar, Rigoni, Sturm and Tamanini as a Dirichlet space having a weak form of Bakry-\'Emery curvature lower bounds in distribution sense. After their work, Braun established a vector calculus for it, in particular, the space of $L^2$-normed $L^{\infty}$-module describing vector fields, $1$-forms, Hessian in $L^2$-sense. In this framework, we establish the Hess-Schrader-Uhlenbrock inequality for $1$-forms as an element of $L^2$-cotangent module (an $L^2$-normed $L^{\infty}$-module), which extends the Hess-Schrader-Uhlenbrock inequality by Braun under an additional condition., Comment: 42 pages. arXiv admin note: substantial text overlap with arXiv:2308.12728. substantial text overlap with arXiv:2108.12374 by other authors