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We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, primarily by boosting the open-circuit voltage (VOC) from 1.06 V to 1.15 V. Adding a post-processing annealing step after C60 evaporation, further improves the fill factor (FF) by 20% from the control to the DAP + post-annealing devices. Using hyperspectral photoluminescence microscopy, we demonstrate that annealing helps improve compositional homogeneity at the top and bottom interfaces of the solar cell, which prevents detrimental bandgap pinning in the devices and improves C60 adhesion. Using time-of-flight secondary ion mass spectrometry, we show that DAP reacts with formamidinium present near the surface of the perovskite lattice to form a larger molecular cation, 1,4,5,6-tetrahydropyrimidinium (THP) that remains at the interface. Combining the use of DAP and the annealing of C60 interface, we fabricate Si-perovskite tandems with PCE of 25.29%, compared to 23.26% for control devices. Our study underscores the critical role of chemical reactivity and thermal post-processing of the C60/Lewis-base passivator interface in minimizing device losses and advancing solar-cell performance of wide-bandgap mixed-cation mixed-halide perovskite for tandem application.

It has been shown that dipolar Bose-Einstein condensates that are tightly trapped along the polarization direction can feature a rich phase diagram. In this paper we show that finite temperature can assist in accessing parts of the phase diagram that otherwise appear hard to realise due to excessively large densities and number of atoms being required. These include honeycomb and stripe phases both in the thermodynamic limit as well as with a finite extent using both variational and numerical calculations. We account for the effect of thermal fluctuations by means of Bogoliubov theory employing the local density approximation. Furthermore, we exhibit real-time evolution simulations leading to such states. We find that finite temperatures can lead to a significant decrease in the necessary particle number and density that might ultimately pave a route for future experimental realisations., Comment: 7 pages, 6 figures

Halide perovskites is a new class of semiconductors with exceptional optoelectronic properties. Among many advantages offered by halide perovskites, the bandgap energy can be tuned in a much broader range than what was possible in conventional semiconductors. This was commonly achieved in previous research by mixing different species of halides into solid solutions. The tuned bandgap using this method, however, often underwent an energy shift under optical or electrical stimuli due to halide segregation. In this work, we discovered an alternative approach to achieve super-bandgap electroluminescence from CsPbBr3. The peak energy of the light emission can be 0.7 eV higher than the reported bandgap energy. Evidence pointed to the radiative recombination at the perovskite-PEDOT:PSS interface being responsible for the unexpected blueshift of electroluminescence. We speculated that perovskite nanocrystals were formed therein and produced higher-energy photons due to quantum confinement. The results suggested an alternative strategy to manipulate and stabilize the color of electroluminescence and achieve particularly blue emission in perovskite-based LEDs.

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE's mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations., Comment: Published version at RitiRAN Workshop - co-located with IEEE VTC Fall 2024

We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR), with Latin, Chinese, or ciphered characters. Detection-based approaches have until now been largely discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with sufficiently diverse data enables learning reasonable character localization for any script; (ii) modern transformer-based detectors can jointly detect a large number of instances, and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet. Our approach, dubbed DTLR, builds on a completely different paradigm than state-of-the-art HTR methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, we demonstrate good performance on a large range of scripts, usually tackled with specialized approaches. In particular, we improve state-of-the-art performances for Chinese script recognition on the CASIA v2 dataset, and for cipher recognition on the Borg and Copiale datasets. Our code and models are available at https://github.com/raphael-baena/DTLR.

Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/., Comment: 10 pages. Webpage: https://airvlab.github.io/cathaction/

Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system's low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed.

This paper investigates the dispersion characteristics of a highly anisotropic metamaterial comprised of metal square patches arranged in a glide symmetry pattern and submerged in vacuum. Theoretical formulas are proposed to describe the electromagnetic tensors of a corresponding uniaxial effective medium with dielectric and magnetic responses. In addition, this work employs theoretical analysis and numerical simulations to examine the interaction between the metamaterial and electromagnetic waves across a broad spectral range. Band diagrams and isofrequency contours show good agreement between theoretical and numerical results for low frequencies and certain directions of propagation at higher frequencies. The ease of designing the metamaterial structure for various applications is facilitated by the derived theoretical formulas, which enable accurate prediction of the electromagnetic response across a wide range of frequencies based on geometric parameters.

We simulate a molecular Bose-Einstein condensate in the strongly dipolar regime, observing the existence of self-bound droplets, as well as their splitting into multiple droplets by confinement-induced frustration. Our quantum Monte Carlo approach goes beyond the limits of the established effective mean-field theories for dipolar quantum gases, revealing small droplets produced by strong dipolar interactions outside known stable regimes. The simulations include realistic molecular interactions and therefore have direct relevance for current and future experiments., Comment: 6 + 1 pages text + Supplementary Material (appendix). 3 figures + 1 table

Program refinement involves correctness-preserving transformations from formal high-level specification statements into executable programs. Traditional verification tool support for program refinement is highly interactive and lacks automation. On the other hand, the emergence of large language models (LLMs) enables automatic code generations from informal natural language specifications. However, code generated by LLMs is often unreliable. Moreover, the opaque procedure from specification to code provided by LLM is an uncontrolled black box. We propose LLM4PR, a tool that combines formal program refinement techniques with informal LLM-based methods to (1) transform the specification to preconditions and postconditions, (2) automatically build prompts based on refinement calculus, (3) interact with LLM to generate code, and finally, (4) verify that the generated code satisfies the conditions of refinement calculus, thus guaranteeing the correctness of the code. We have implemented our tool using GPT4, Coq, and Coqhammer, and evaluated it on the HumanEval and EvalPlus datasets.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab (ARC), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) near-real-time RIC. We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink., Comment: 15 pages, 15 figures, 3 tables. arXiv admin note: text overlap with arXiv:2310.17062

Augmented reality (AR) has the potential to improve the immersion and efficiency of computer-assisted orthopaedic surgery (CAOS) by allowing surgeons to maintain focus on the operating site rather than external displays in the operating theatre. Successful deployment of AR to CAOS requires a calibration that can accurately calculate the spatial relationship between real and holographic objects. Several studies attempt this calibration through manual alignment or with additional fiducial markers in the surgical scene. We propose a calibration system that offers a direct method for the calibration of AR head-mounted displays (HMDs) with CAOS systems, by using infrared-reflective marker-arrays widely used in CAOS. In our fast, user-agnostic setup, a HoloLens 2 detected the pose of marker arrays using infrared response and time-of-flight depth obtained through sensors onboard the HMD. Registration with a commercially available CAOS system was achieved when an IR marker-array was visible to both devices. Study tests found relative-tracking mean errors of 2.03 mm and 1.12{\deg} when calculating the relative pose between two static marker-arrays at short ranges. When using the calibration result to provide in-situ holographic guidance for a simulated wire-insertion task, a pre-clinical test reported mean errors of 2.07 mm and 1.54{\deg} when compared to a pre-planned trajectory., Comment: Published in conference proceedings for 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). For associated code visit: https://github.com/HL2-DINO

We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on HST/WFPC2 exposures. Previously, we demonstrated that our DL model can eliminate the pixel-phase bias otherwise present in these undersampled images; however that analysis was limited to the central portion of each detector. In the current work we introduce the inclusion of global positions to account for the PSF variation across the entire chip and instrumental magnitudes to account for nonlinear effects such as charge transfer efficiency. The DL model is trained using a unique series of WFPC2 observations of globular cluster 47 Tuc, data sets comprising over 600 dithered exposures taken in each of two filters, F555W and F814W. It is found that the PSF variations across each chip correspond to corrections of the order of 100 mpix, while magnitude effects are at a level of about 10 mpix. Importantly, pixel-phase bias is eliminated with the DL model; whereas, with a classic centering algorithm, the amplitude of this bias can be up to 40 mpix. Our improved DL model yields star-image centers with uncertainties of 8-10 mpix across the full field of view of WFPC2., Comment: accepted for publication by PASP

Polariton condensation relies on the macroscopic occupation of the lowest-energy polariton state beyond a critical density. The mechanisms driving the occupation and depopulation of this state all rely on multi-particle scattering, whose dynamics determine the extent to which condensates can form spontaneously. To pinpoint many-body processes hindering polariton condensation in two-dimensional metal-halide semiconductors, we examine the exciton-polariton dynamics in a Fabry-P\'erot microcavity over timescales involving polariton ($\bm{\ll 1}$\,ps) and exciton scattering ($\bm{\gg 1}$\,ps). We find enhanced nonlinear exciton-exciton interactions in the microcavity versus the bare semiconductor and ultrafast polariton scattering depopulating the lowest-energy polariton state. We posit that the complex scattering landscape between the exciton reservoir and polaritons limits the formation of polariton condensates in these semiconductors, and we discuss the generality of our conclusions for highly polar materials in which the lattice mediates multi-particle correlations., Comment: 27 pages, 6 figures, plus supplementary material document. Submitted for publication

Context: Open clusters' dynamical evolution is driven by stellar evolution, internal dynamics and external forces, which according to dynamical simulations, will evaporate them in a timescale of about 1 Ga. However, about 10\% of the known open clusters are older. They are special systems whose detailed properties are related to their dynamical evolution and the balance between mechanisms of cluster formation and dissolution. Aims:We investigate the spatial distribution and structural parameters of six open clusters older than 1 Ga in order to constrain their dynamical evolution, and longevity.} Methods: We identify members using Gaia EDR3 data up to a distance of 150 pc from each cluster's centre. We investigate the spatial distribution of stars inside each cluster to understand their degree of mass segregation. Finally, in order to interpret the obtained radial density profiles we reproduced them using the lowered isothermal model explorer with PYTHON Limepy and spherical potential escapers stitched SPES. Results: All the studied clusters seem more extended than previously reported in the literature. The spatial distributions of three of them show some structures aligned with their orbits. They may be related to the existence of extra tidal stars. In fact, we find that about 20 % of their members have enough energy to leave the systems or are already unbound. Together with their initial masses, their distances to the Galactic plane may play significant roles in their survival. We found clear evidences that the most dynamically evolved clusters do not fill their Roche volumes, appearing more concentrated than the others. Finally, we find a cusp-core dichotomy in the central regions of the studied clusters, which shows some similarities to the one observed among globular clusters., Comment: 16 pages, 11 figures, accepted for publication in A&A

In the regularity theory of solutions to elliptic partial differential equations often the concept of stability plays the role of a sufficient condition for smoothness. It is a natural question to ask if this holds true for nonstable but finite Morse index solutions. We provide a negative answer showing the existence of sequences of solutions with radial Morse index equal to 1 for which regularity estimates can not be satisfied., Comment: 8 pages

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's health due to prolonged radiation exposure. As an alternative, interventional ultrasound has notable benefits such as being radiation-free, fast to deploy, and having a small footprint in the operating room. Yet, ultrasound is hard to interpret, and highly prone to artifacts and noise. Additionally, interventional radiologists must undergo extensive training before they become qualified to diagnose and treat patients effectively, leading to a shortage of staff, and a lack of open-source datasets. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary map estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future., Comment: This work has been submitted to the IEEE for possible publication

In classification, it is usual to observe that models trained on a given set of classes can generalize to previously unseen ones, suggesting the ability to learn beyond the initial task. This ability is often leveraged in the context of transfer learning where a pretrained model can be used to process new classes, with or without fine tuning. Surprisingly, there are a few papers looking at the theoretical roots beyond this phenomenon. In this work, we are interested in laying the foundations of such a theoretical framework for transferability between sets of classes. Namely, we establish a partially ordered set of subsets of classes. This tool allows to represent which subset of classes can generalize to others. In a more practical setting, we explore the ability of our framework to predict which subset of classes can lead to the best performance when testing on all of them. We also explore few-shot learning, where transfer is the golden standard. Our work contributes to better understanding of transfer mechanics and model generalization.

Hybrid organometal halide perovskites (HP) present exceptional optoelectronic properties, but their poor long-term stability is a major bottleneck for their commercialization. Herein, we present a solvent-free approach to growing single-crystal organic nanowires (ONW), nanoporous metal oxide scaffolds, and HP to form a core@multishell architecture. The synthetic procedure is carried out under mild vacuum conditions employing thermal evaporation for the metal-free phthalocyanine (H2Pc) nanowires, which will be the core, plasma-enhanced chemical vapor deposition (PECVD) for the TiO2 shell, and co-evaporation of lead iodide (PbI2) and methylammonium iodide (CH3NH3I / MAI) for the CH3NH3PbI3 (MAPbI3 / MAPI) perovskite shell. We present a detailed characterization of the nanostructures by (S)-TEM and XRD, revealing a different crystallization of the hybrid perovskite depending on the template: while the growth on H2Pc nanowires induces the typical tetragonal structure of the MAPI perovskite, a low-dimensional phase (LDP) was observed on the one-dimensional TiO2 nanotubes. Such a combination yields an unprecedentedly stable photoluminescence emission over 20 hours and over 300 hours after encapsulation in polymethyl methacrylate (PMMA) under different atmospheres including N2, air, and high moisture levels. In addition, the unique one-dimensional morphology of the system, together with the high refractive index HP, allows for a strong waveguiding effect along the nanowire length.

We prove a pointwise estimate for the decreasing rearrangement of $Tf$, where $T$ is any sublinear operator satisfying the weak-type boundedness $$ T:L^{p,1}(\mu) \to L^{p,\infty}(\nu), \quad \forall p: 1

We shall prove pointwise estimates for the decreasing rearrangement of $Tf$, where $T$ covers a wide range of interesting operators in Harmonic Analysis such as operators satisfying a Fefferman-Stein inequality, the Bochner-Riesz operator, rough operators, sparse operators, Fourier multipliers, etc. In particular, our main estimate is of the form $$ (Tf)^*(t) \leq C\left( \frac 1t\int_0^tf^*(s)\,ds + \int_t^\infty \left( 1 + \log\frac st \right)^{- 1}\varphi \left(1 + \log\frac st \right) f^*(s)\,\frac{ds}s \right), $$ where $\varphi$ is determined by the Muckenhoupt $A_p$-weight norm behaviour of the operator., Comment: 23 pages

We analyse the finite-temperature phase diagram of a dipolar Bose Einstein Condensate confined in a tubular geometry. The effect of thermal fluctuations is accounted for by means of Bogoliubov theory employing the local density approximation. In the considered geometry, the superfluid-supersolid phase transition can be of first- and second-order. We discuss how the corresponding transition point is affected by the finite temperature of the system., Comment: 6 pages, 7 figures

We study the interplay between the anisotropy of the dipole-dipole interaction and confinement in a curved geometry by means of the extended Gross-Pitaevskii equation, which allows us to characterize the ground state of a dipolar Bose gas under the confinement of a bubble trapping potential. We do so in terms of the scattering length a and the number of particles. We observe the emergence of a wide variety of dipolar solids, consisting on arrangements of different number of droplets along a ring over the equator of the spherical shell confinement. We also show that the transition between the different phases of the system can be engineered by varying a, the number of particles or the radius of the trap, parameters which can be experimentally tuned. Finally, we show the importance of working in microgravity conditions as gravity unstabilizes the observed dipolar solids., Comment: New version, 10 pages, 10 figures

We have investigated the properties (e.g., age, metallicity) of the stellar populations of a ring-like tidal stellar stream (or streams) around the edge-on galaxy SPRC047 (z = 0.031) using spectral energy distribution (SED) fits to integrated broad-band aperture flux densities. We used visual images in six different bands and Spitzer/IRAC 3.6 micron data. We have attempted to derive best-fit stellar population parameters (metallicity, age) in three non-contiguous segments of the stream. Due to the very low surface brightness of the stream, we have performed a deconvolution with a Richardson-Lucy type algorithm of the low spatial resolution 3.6 micron IRAC image, thereby reducing the effect of the point-spread-function (PSF) aliased "emission" from the bright edge-on central galaxy at the locations of our three stream segments. Our SED fits that used several different star formation history priors, from an exponentially decaying star formation burst to continuous star formation, indicate that the age-metallicity-dust degeneracy is not resolved, most likely because of inadequate wavelength coverage and low signal-to-noise ratios of the low surface brightness features. We also discuss how future deep visual-near-infrared observations, combined with absolute flux calibration uncertainties at or below the 1 per cent level, complemented by equally well absolute flux calibrated observations in ultraviolet and mid-infrared bands, would improve the accuracy of broad-band SED fitting results for low surface brightness targets, such as stellar streams around nearby galaxies that are not resolved into stars., Comment: Accepted for publication in the Astrophysical Journal