Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

Abstract
Extreme Learning Machines (ELMs) are a versatile Machine Learning (ML) algorithm that features as the main advantage the possibility of a seamless implementation with physical systems. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding in regard to their optical implementations. In this context, this work makes use of an optical complex media and wavefront shaping techniques to implement a versatile optical ELM playground to gain a deeper insight into these machines. In particular, we present experimental evidences on the correlation between the effective dimensionality of the hidden space and its generalization capability, thus bringing the inner workings of optical ELMs under a new light and opening paths toward future technological implementations of similar principles.

Abstract
The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

Abstract
Many physical and chemical processes of interest evolve on timescales that push the limits of conventional spec troscopic instrumentation. Indeed, the temporal resolution of standard spectrometers is often insufficient to track these dynamics, which is connected to the fact that most systems rely on frame-based sensors, imposing funda mental constraints on acquisition speed, sensitivity, and data efficiency, frequently limiting practical operation to the kilohertz regime. In this work, we present an approach to circumvent this limitation by developing an event-based spectrometer to enable spectral reconstruction with microsecond temporal resolution by leveraging a Czerny-Turner configuration combined with asynchronous and event-driven sensing. A dedicated signal process ing pipeline converts the resulting stream of binary events into calibrated spectra through temporal accumulation, geometric correction, and vertical spatial integration of the spectral line, covering a 234 nm bandwidth in the visible range with an estimated spectral dispersion of approximately 0.18 nm per pixel. Performance characteri zation under temporally modulated illumination demonstrates that the event-based spectrometer can reconstruct spectra at probing rates of up to tens of kilohertz, far exceeding the practical limits of a conventional frame-based spectrometer operated in parallel, while accurately preserving spectral peak positions and relative spectral fea tures. Finally, to further illustrate its potential applications, the system is validated in a microfluidic experiment integrated into an inverted microscope, where spectral changes induced by an absorbing dye are tracked with higher temporal fidelity and resolution compared with the frame-based approach. These results establish event-based spectroscopy as a promising paradigm for real-time, high-temporal-resolution spectral measurements in dynamic and low-light applications.

Abstract
A superfluid flows without friction below a critical velocity, exhibiting zero drag force on impurities. Above this threshold, superfluidity breaks down, and the internal energy is redistributed into incoherent excitations such as vortices. We demonstrate that a mobile, finite-mass impurity immersed in a flowing two-dimensional paraxial superfluid of light can swim against the superfluid current when the critical velocity is exceeded. This self-propulsion is achieved by the periodic emission of vortex-antivortex pairs downstream, which impart an upstream recoil momentum that results in a net propulsive force. Analogous to biological systems that minimize effort by exploiting wake turbulence, the impurity harnesses this vortex backreaction as a passive mechanism of locomotion. Based on a simple theoretical model, we quantitatively describe how this mechanism depends on the impurity geometry and the surrounding flow velocity. Our findings establish a fundamental link between internal-energy dissipation in quantum fluids and concepts of self-propulsion in active-matter systems and open new possibilities for exploiting quantum vortices for controlled transport at the microscale.