Topological order and thermal equilibrium in polariton condensates

We report the observation of the Berezinskii-Kosterlitz-Thouless transition for a 2D gas of exciton-polaritons, and through the joint measurement of the first-order coherence both in space and time we bring compelling evidence of a thermodynamic equilibrium phase transition in an otherwise open driven/dissipative system. This is made possible thanks to long polariton lifetimes in high-quality samples with small disorder and in a reservoir-free region far away from the excitation spot, that allow topological ordering to prevail. The observed quasi-ordered phase, characteristic for an equilibrium 2D bosonic gas, with a decay of coherence in both spatial and temporal domains with the same algebraic exponent, is reproduced with numerical solutions of stochastic dynamics, proving that the mechanism of pairing of the topological defects (vortices) is responsible for the transition to the algebraic order. Finally, measurements in the weak-coupling regime confirm that polariton condensates are fundamentally different from photon lasers and constitute genuine quantum degenerate macroscopic states

Safety and Performance of RHA4 in the Midface Using the Multilayering Technique: Preclinical and Clinical Evidence.

Resilient hyaluronic acid (RHA) fillers are used to treat dynamic wrinkles or provide tissue lifting in facial aesthetics. This study explored the biological, biomechanical, and clinical safety and performance of RHA4, a volumizing hyaluronic acid filler tailored for tissue support in dynamic facial areas, upon interaction with human subcutaneous adipose tissue (AT). RHA4 underwent cytocompatibility testing with human fibroblasts and adipose stem cells. A 1-year rat in vivo implantation study tracked tissue integration, local effects, and filler degradation. Biomechanical tests assessed RHA4's impact on subcutaneous AT mechanics. Clinical outcomes, safety, injection volumes, and techniques were evaluated in 35 patients, treated in midface deep and superficial fat compartments via a multilayering methodology. Dynamic outcomes and 2-year follow-up of RHA4 in the midface using multilayer treatments were described. RHA4 demonstrated excellent biocompatibility and tissue integration both in vitro and in vivo, exhibiting minimal local inflammation and rapid collagen bundle formation within the filler. It integrated gradually over time and was well tolerated, allowing for increased extracellular matrix presence, neovascularization, denser collagen deposition, and AT growth. Ex vivo, RHA4 did not impede fat motion biomechanics but visibly lifted the tissue. Clinically, RHA4 proved safe and effective for lifting both deep and superficial fat compartments in the midface without affecting facial expressiveness. Preclinical and clinical evidence confirmed that RHA4 is a versatile filler capable of lifting tissue efficiently, whether deep or superficial, particularly through the multilayering treatment approach. Importantly, RHA4 preserves AT biomechanics, adapts to the dynamism of the face, and ensures natural-looking outcomes

Synaptic tagging and capture in the living rat

In isolated hippocampal slices, decaying long-term potentiation can be stabilized and converted to late long-term potentiation lasting many hours, by prior or subsequent strong high-frequency tetanization of an independent input to a common population of neurons—a phenomenon known as ‘synaptic tagging and capture’. Here we show that the same phenomenon occurs in the intact rat. Late long-term potentiation can be induced in CA1 during the inhibition of protein synthesis if an independent input is strongly tetanized beforehand. Conversely, declining early long-term potentiation induced by weak tetanization can be converted into lasting late long-term potentiation by subsequent strong tetanization of a separate input. These findings indicate that synaptic tagging and capture is not limited to in vitro preparations; the past and future activity of neurons has a critical role in determining the persistence of synaptic changes in the living animal, thus providing a bridge between cellular studies of protein synthesis-dependent synaptic potentiation and behavioural studies of memory persistence

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Ultra-low-power hybrid light-matter solitons.

New functionalities in nonlinear optics will require systems with giant optical nonlinearity as well as compatibility with photonic circuit fabrication techniques. Here we introduce a platform based on strong light-matter coupling between waveguide photons and quantum-well excitons. On a sub-millimetre length scale we generate picosecond bright temporal solitons at a pulse energy of only 0.5 pJ. From this we deduce a nonlinear refractive index three orders of magnitude larger than in any other ultrafast system. We study both temporal and spatio-temporal nonlinear effects and observe dark-bright spatio-temporal polariton solitons. Theoretical modelling of soliton formation in the strongly coupled system confirms the experimental observations. These results show the promise of our system as a high speed, low power, integrated platform for physics and devices based on strong interactions between photons

Real-space collapse of a polariton condensate

Microcavity polaritons are two-dimensional bosonic fluids with strong nonlinearities, composed of coupled photonic and electronic excitations. In their condensed form, they display quantum hydrodynamic features similar to atomic Bose–Einstein condensates, such as long-range coherence, superfluidity and quantized vorticity. Here we report the unique phenomenology that is observed when a pulse of light impacts the polariton vacuum: the fluid which is suddenly created does not splash but instead coheres into a very bright spot. The real-space collapse into a sharp peak is at odd with the repulsive interactions of polaritons and their positive mass, suggesting that an unconventional mechanism is at play. Our modelling devises a possible explanation in the self-trapping due to a local heating of the crystal lattice, that can be described as a collective polaron formed by a polariton condensate. These observations hint at the polariton fluid dynamics in conditions of extreme intensities and ultrafast times