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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.