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This study aims to advance surgical phase recognition in arthroscopic procedures, specifically Anterior Cruciate Ligament (ACL) reconstruction, by introducing the first arthroscopy dataset and developing a novel transformer-based model. We aim to establish a benchmark for arthroscopic surgical phase recognition by leveraging spatio-temporal features to address the specific challenges of arthroscopic videos including limited field of view, occlusions, and visual distortions. We developed the ACL27 dataset, comprising 27 videos of ACL surgeries, each labeled with surgical phases. Our model employs a transformer-based architecture, utilizing temporal-aware frame-wise feature extraction through a ResNet-50 and transformer layers. This approach integrates spatio-temporal features and introduces a Surgical Progress Index (SPI) to quantify surgery progression. The model's performance was evaluated using accuracy, precision, recall, and Jaccard Index on the ACL27 and Cholec80 datasets. The proposed model achieved an overall accuracy of 72.91% on the ACL27 dataset. On the Cholec80 dataset, the model achieved a comparable performance with the state-of-the-art methods with an accuracy of 92.4%. The SPI demonstrated an output error of 10.6% and 9.86% on ACL27 and Cholec80 datasets respectively, indicating reliable surgery progression estimation. This study introduces a significant advancement in surgical phase recognition for arthroscopy, providing a comprehensive dataset and a robust transformer-based model. The results validate the model's effectiveness and generalizability, highlighting its potential to improve surgical training, real-time assistance, and operational efficiency in orthopedic surgery. The publicly available dataset and code will facilitate future research and development in this critical field.

Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly manual labeling by experts, limiting dataset size and model generalizability. Additionally, the complexity of ultrasound physics and acoustic shadow makes the images difficult for humans to interpret, leading to incomplete labels in anechoic regions and limiting model performance. To advance ultrasound bone segmentation and establish effective model benchmarks, larger and higher-quality datasets are needed. We propose a methodology for collecting ex-vivo ultrasound datasets with automatically generated bone labels, including anechoic regions. The proposed labels are derived by accurately superimposing tracked bone CT models onto the tracked ultrasound images. These initial labels are refined to account for ultrasound physics. A clinical evaluation is conducted by an expert physician specialized on orthopedic sonography to assess the quality of the generated bone labels. A neural network for bone segmentation is trained on the collected dataset and its predictions are compared to expert manual labels, evaluating accuracy, completeness, and F1-score. We collected the largest known dataset of 100k ultrasound images of human lower limbs with bone labels, called UltraBones100k. A Wilcoxon signed-rank test with Bonferroni correction confirmed that the bone alignment after our method significantly improved the quality of bone labeling (p < 0.001). The model trained on UltraBones100k consistently outperforms manual labeling in all metrics, particularly in low-intensity regions (320% improvement in completeness at a distance threshold of 0.5 mm).

Advances in computer vision, particularly in optical image-based 3D reconstruction and feature matching, enable applications like marker-less surgical navigation and digitization of surgery. However, their development is hindered by a lack of suitable datasets with 3D ground truth. This work explores an approach to generating realistic and accurate ex vivo datasets tailored for 3D reconstruction and feature matching in open orthopedic surgery. A set of posed images and an accurately registered ground truth surface mesh of the scene are required to develop vision-based 3D reconstruction and matching methods suitable for surgery. We propose a framework consisting of three core steps and compare different methods for each step: 3D scanning, calibration of viewpoints for a set of high-resolution RGB images, and an optical-based method for scene registration. We evaluate each step of this framework on an ex vivo scoliosis surgery using a pig spine, conducted under real operating room conditions. A mean 3D Euclidean error of 0.35 mm is achieved with respect to the 3D ground truth. The proposed method results in submillimeter accurate 3D ground truths and surgical images with a spatial resolution of 0.1 mm. This opens the door to acquiring future surgical datasets for high-precision applications., Comment: 18 pages, 12 figures. Submitted to the 16th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2025)

In this perspective article, we revisit and critically evaluate prevailing viewpoints on the capabilities and limitations of near-term quantum computing and its potential transition toward fully fault-tolerant quantum computing. We examine theoretical no-go results and their implications, addressing misconceptions about the practicality of quantum error mitigation techniques and variational quantum algorithms. By emphasizing the nuances of error scaling, circuit depth, and algorithmic feasibility, we highlight viable near-term applications and synergies between error mitigation and early fault-tolerant architectures. Our discussion explores strategies for addressing current challenges, such as barren plateaus in variational circuits and the integration of quantum error mitigation and quantum error correction techniques. We aim to underscore the importance of continued innovation in hardware and algorithmic design to bridge the gap between theoretical potential and practical utility, paving the way for meaningful quantum advantage in the era of late noisy intermediate scale and early fault-tolerant quantum devices., Comment: Comments welcome

An essential component for the Fifth Generation of Mobile Networks deployments is the 5G Core (5GC), which bridges the 5G Radio Access Network (RAN) to the rest of the Internet. Some open-source platforms for the 5GC have emerged and been deployed in Common Off-the-Shelf (COTS)-based setups. Despite these open-source 5GC initiatives following the 3GPP specifications, they differ in implementing some features and their stages in the timeline of 3GPP releases. Besides that, they may yield different performance to metrics related to the data and control planes. This article reviews the major open-source 5GC platforms and evaluates their performance in a 5G Standalone (SA) COTS-based testbed. The results indicate that Open5GS provides the best latencies for control plane procedures, OpenAirInterface offers the highest data rates, and Free5GC has the lowest resource consumption., Comment: Accepted for publication in ICOIN 2025

The certification of randomness is essential for both fundamental science and information technologies. Unlike traditional random number generators, randomness obtained from nonlocal correlations is fundamentally guaranteed to be unpredictable. However, it is also highly susceptible to noise. Here, we show that extending the conventional bipartite Bell scenario to hybrid quantum networks -- which incorporate both quantum channels and entanglement sources -- enhances the robustness of certifiable randomness. Our protocol even enables randomness to be certified from Bell-local states, broadening the range of quantum states useful for this task. Through both theoretical analysis and experimental validation in a photonic network, we demonstrate enhanced performance and improved noise resilience., Comment: 14 pages, 6 figures

A known issue for future nuclear reactors is helium accumulation inside the steel structure materials, responsible for structural issues such as embrittlement and cracking. One possible solution is using new types of reinforced steel, such as oxide dispersion strengthened (ODS) steel. It consists of adding oxide nanoparticles to the Fe-based material, especially yttrium oxide (yttria, Y$_2$O$_3$), improving its properties. Therefore, one first step is understanding the helium diffusion inside this system. Very little is known about helium inside yttria, with most studies being theoretical ones. Based on this context, this work proposes a combined theoretical and experimental multiscale approach to investigate helium diffusion inside yttria. The theoretical approach starts with the density functional theory, used to model the atomic yttria cell and determine helium insertion sites. The transitions between the sites were described using the NEB method. Kinetic Monte Carlo was then employed to obtain the interstitial diffusion coefficient expression. It showed a limited diffusion at temperatures below 600 K, which may indicate a tendency for He to be blocked in the oxide. Then, the charged vacancies were explored. It showed that the vacancy further reduces helium diffusion. Finally, it was demonstrated that helium spreads across different vacancies and interstitial sites. The experimental part involved implanting helium ions at 50 keV in samples with nanometric or micrometric grains. Then, the specimens were characterised with transmission electron microscopy (TEM) and thermo-desorption spectroscopy (TDS) techniques. TEM did not evidence detectable bubbles even at the highest studied fluence (1\time 10^{16}$ cm$^{-2}$). The TDS highlighted different mechanisms for helium diffusion and the grain size's role, providing a model for diffusion coefficient calculation based on interstitial diffusion.

This paper describes use of model checking to verify synchronisation properties of an industrial welding system consisting of a cobot arm and an external turntable. The robots must move synchronously, but sometimes get out of synchronisation, giving rise to unsatisfactory weld qualities in problem areas, such as around corners. These mistakes are costly, since time is lost both in the robotic welding and in manual repairs needed to improve the weld. Verification of the synchronisation properties has shown that they are fulfilled as long as assumptions of correctness made about parts outside the scope of the model hold, indicating limitations in the hardware. These results have indicated the source of the problem, and motivated a re-calibration of the real-life system. This has drastically improved the welding results, and is a demonstration of how formal methods can be useful in an industrial setting., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability. In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach., Comment: preprint, 8 pages, 2024

This paper delves into the deformation of spinor structures within nontrivial topologies and their physical implications. The deformation is modeled by introducing real functions that modify the standard spinor dynamics, leading to distinct physical regions characterized by varying degrees of Lorentz symmetry violation. It allows us to investigate the effects in the dynamical equation and a geometrized nonlinear sigma model. The findings suggest significant implications for the spinor fields in regions with nontrivial topologies, providing a robust mathematical approach to studying exotic spinor behavior., Comment: 15 pages, no figures, accepted for publication in the Journal of Physics A: Mathematical and Theoretical

Virtual Reality technology, when integrated with Surgical Digital Twins (SDTs), offers significant potential in medical training and surgical planning. We present SurgTwinVR, a VR application that immerses users within an SDT and enables them to navigate a high-fidelity virtual replica of the surgical environment. SurgTwinVR is the first VR application to utilize a dynamic 3D environment that is a clone of a real surgery, encompassing the entire surgical scene, including the surgeon, anatomy, and instruments. Our system utilizes a SDT with important improvements for real-time rendering and features to showcase the potential benefits of such an application in surgical education., Comment: Accepted demo at ISMAR 2024. Project page: https://jonashein.github.io/surgerydigitization/

Achieving high-precision measurements on near-term quantum devices is critical for advancing quantum computing applications. In this paper, we explore several practical techniques to enhance measurement accuracy using randomized measurements, focusing on minimizing shot overhead, circuit overhead, measurement noise, and time-dependent measurement noise. Our approach leverages locally biased random measurements to reduce shot overhead, in addition to repeated settings and parallel quantum detector tomography to reduce circuit overhead and mitigate measurement noise. Additionally, we employ a blended scheduling technique to mitigate time-dependent measurement noise. We demonstrate the effectiveness of these techniques through a case study on the molecular energy estimation of the BODIPY molecule using the Hartree-Fock state on an IBM Eagle r3 computer, showcasing significant improvements in measurement precision. These strategies pave the way for more reliable and accurate quantum computations, particularly in applications requiring precise molecular energy calculations., Comment: 15 pages, 8 figures

Ethereum is a distributed, peer-to-peer blockchain infrastructure that has attracted billions of dollars. Perhaps due to its success, Ethereum has become a target for various kinds of attacks, motivating researchers to explore different techniques to identify vulnerabilities in EVM bytecode (the language of the Ethereum Virtual Machine), including formal verification, symbolic execution, and fuzz testing. Although recent studies empirically compare smart contract fuzzers, there is a lack of literature investigating how simpler greybox fuzzers compare to more advanced ones. To fill this gap, in this paper, we present DogeFuzz, an extensible infrastructure for fuzzing Ethereum smart contracts, currently supporting black-box fuzzing and two grey-box fuzzing strategies: coverage-guided grey-box fuzzing (DogeFuzz-G) and directed grey-box fuzzing (DogeFuzz-DG). We conduct a series of experiments using benchmarks already available in the literature and compare the DogeFuzz strategies with state-of-the-art fuzzers for smart contracts. Surprisingly, although DogeFuzz does not leverage advanced techniques for improving input generation (such as symbolic execution or machine learning), DogeFuzz outperforms sFuzz and ILF, two state-of-the-art fuzzers. Nonetheless, the Smartian fuzzer shows higher code coverage and bug-finding capabilities than DogeFuzz., Comment: 16 pages,published in SBSEG 2024 conference

Backtracking line search is foundational in numerical optimization. The basic idea is to adjust the step size of an algorithm by a constant factor until some chosen criterion (e.g. Armijo, Goldstein, Descent Lemma) is satisfied. We propose a new way for adjusting step sizes, replacing the constant factor used in regular backtracking with one that takes into account the degree to which the chosen criterion is violated, without additional computational burden. For convex problems, we prove adaptive backtracking requires fewer adjustments to produce a feasible step size than regular backtracking does for two popular line search criteria: the Armijo condition and the descent lemma. For nonconvex smooth problems, we additionally prove adaptive backtracking enjoys the same guarantees of regular backtracking. Finally, we perform a variety of experiments on over fifteen real world datasets, all of which confirm that adaptive backtracking often leads to significantly faster optimization.

Recently the Local Friendliness (LF) no-go theorem has gained a lot of attention, owing to its deep foundational implications. This no-go theorem applies to scenarios which combine Bell experiments with Wigner's friend-type set ups, containing space-like separated superobservers who are assumed to be capable of performing quantum operations on a local observer, also known as their "friend". Analogously to the hypothesis of local hidden variables in Bell scenarios, a set of assumptions termed "Local Friendliness" constrains the space of probabilistic behaviours accessible to the superobservers to be a particular subset of the no-signalling polytope in such scenarios. It has additionally been shown, that there are scenarios where the set of behaviours compatible with Local Friendliness is strictly larger than the Bell-local polytope, while in some scenarios those sets are equal. In this work, we complete the picture by identifying all the canonical Local Friendliness scenarios, with arbitrary but finite numbers of superobservers, friends, measurements and outcomes, where the set of LF correlations admits a local hidden variable model, and where they do not. Our proof is constructive in the sense that we also demonstrate how a local hidden variable model can be constructed, given a behaviour compatible with LF in the appropriate scenarios. While our principal motivation is the foundational question of better understanding the constraints from Local Friendliness, the same inequalities constraining LF polytopes have been shown to arise in a priori unrelated contexts of device-independent information processing. Our results may thus find use in those research areas as well., Comment: 16 pages, 5 figures

Structured merge tools exploit programming language syntactic structure to enhance merge accuracy by reducing spurious conflicts reported by unstructured tools. By creating and handling full ASTs, structured tools are language-specific and harder to implement. They can also be computationally expensive when merging large files.To reduce these drawbacks, semistructured merge tools work with partial ASTs that use strings to represent lower level syntactic structures such as method bodies, and rely on unstructured tools to merge them. This, however, results in merge accuracy loss. To improve accuracy without compromising semistructured merge benefits, we propose a tool that leverages language-specific syntactic separators to infer structure without parsing. We still resort to an unstructured tool to merge lower level structures, but only after preprocessing the code so that text in between separators such as curly braces appear in separate lines. This way we emulate the capabilities of structured merge tools while avoiding their drawbacks. By comparing our tool with a robust implementation of semistructured merge, we find that our tool substantially reduces the number of spurious conflicts. We also observe significant but less substantial reductions on the overall number of reported conflicts, and of files with conflicts. However, similar to structured tools, our tool lets more merge conflicts go undetected. Our tool shows significant improvements over unstructured tools widely used in practice. Finally we observe that exploiting language-specific syntactic separators introduces unique textual alignment challenges.

We propose a method for providing unbiased estimators of multiple observables with low statistical error by utilizing informationally (over)complete measurements and tensor networks. The technique consists of an observable-specific classical optimization of the measurement data based on tensor networks leading to low-variance estimations. Compared to other observable estimation protocols based on classical shadows and measurement frames, our approach offers several advantages: (i) it can be optimized to provide lower statistical error, resulting in a reduced measurement budget to achieve a specified estimation precision; (ii) it scales to a large number of qubits due to the tensor network structure; (iii) it can be applied to any measurement protocol with measurement operators that have an efficient representation in terms of tensor networks. We benchmark the method through various numerical examples, including spin and chemical systems in both infinite and finite statistics scenarios, and show how optimal estimation can be found even when we use tensor networks with low bond dimensions., Comment: 20 pages, 4 figures. Updated version with corrected typos and minor modifications

We prove the equivalence between the several notions of generalized Ricci curvature found in the literature. As an application, we characterize when the total generalized Ricci tensor is symmetric., Comment: 10 pages, to appear in Proc. Amer. Math. Soc

A Software-Defined Radio (SDR) platform is a communication system that implements as software functions that are typically implemented in dedicated hardware. One of its main advantages is the flexibility to test and deploy radio communication networks in a fast and cheap way. In the context of the Fifth Generation (5G) of wireless cellular networks, there are open source SDR platforms available online. Two of the most popular SDR platforms are srsRAN and OpenAirInterface. This paper presents these two platforms, the characteristics of the networks created by them, the possibilities of changes in their interfaces and configurations, and also their limits. Moreover, in this paper, we also evaluate and compare both platforms in an experimental setup deployed in a laboratory., Comment: Published at XLI BRAZILIAN SYMPOSIUM ON TELECOMMUNICATIONS AND SIGNAL PROCESSING - SBrT 2023

The understanding of open quantum systems is crucial for the development of quantum technologies. Of particular relevance is the characterisation of divisible quantum dynamics, seen as a generalisation of Markovian processes to the quantum setting. Here, we propose a way to detect divisibility and quantify how non-divisible a quantum channel is through the concept of channel discrimination. We ask how well we can distinguish generic dynamics from divisible dynamics. We show that this question can be answered efficiently through semidefinite programming, which provides us with an operational and efficient way to quantify divisibility., Comment: 13 pages. 5 figures. Several appendices. Suggestions are welcome!

Topological Spherical T-duality was introduced by Bouwknegt, Evslin and Mathai in [BEM15] as an extension of topological T-duality from $S^1$-bundles to $\mathrm{SU}(2)$-bundles endowed with closed 7-forms. This notion was further extended to sphere bundles by Lind, Sati and Westerland [LSW16] as a duality between $S^{2n-1}$-bundles endowed with closed $(4n-1)$-forms. We generalise this relation one step further and define T-duality for $S^{2n-1}$-bundles endowed with closed odd forms of arbitrary degree. The degree of the form determines the dimension of the fibers of the dual spaces. We show that $T$-duals exist and, as in the previous cases, $T$-dual spaces have isomorphic twisted cohomology. We finish by introducing a version of Courant algebroids which is compatible with spherical T-duality., Comment: 22 pages

The Lounesto classification is a well-established scheme for categorizing spinors based on their physical content, which are determined by their associated bilinear forms. It consists of six disjoint classes encompassing the known spinors within the context of the standard model of high-energy physics. However, advancements in theories beyond the standard model have opened the door to potential new spinorial adjoint structures, leading to new unforeseen classess. These developments indicate the potential for extending the standard Lounesto classification. In this paper, we explore all possible subclasses that could extend the Lounesto scheme. We highlight the most relevant subclasses by imposing constraints to their corresponding dual structures, thus broadening our understanding of spinor and its applications in theoretical physics., Comment: 10 pages; 5 tables

We present an interactive Web platform that, given a directed graph, allows identifying the most relevant nodes related to a given query node. Besides well-established algorithms such as PageRank and Personalized PageRank, the demo includes Cyclerank, a novel algorithm that addresses some of their limitations by leveraging cyclic paths to compute personalized relevance scores. Our demo design enables two use cases: (a) algorithm comparison, comparing the results obtained with different algorithms, and (b) dataset comparison, for exploring and gaining insights into a dataset and comparing it with others. We provide 50 pre-loaded datasets from Wikipedia, Twitter, and Amazon and seven algorithms. Users can upload new datasets, and new algorithms can be easily added. By showcasing efficient algorithms to compute relevance scores in directed graphs, our tool helps to uncover hidden relationships within the data, which makes of it a valuable addition to the repertoire of graph analysis algorithms., Comment: 4 pages, 1 figure. To appear at 2024 IEEE 40th International Conference on Data Engineering (ICDE)

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way., Comment: preprint, 4 pages

Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. As these requirements are typically defined by non-technical system stakeholders with different expertise and priorities (ethicists, lawyers, social scientists, etc.), ensuring their well-formedness and consistency is very challenging. Recent research has tackled this challenge using a domain-specific language to specify normative requirements as rules whose consistency can then be analysed with formal methods. In this paper, we propose a complementary approach that uses Large Language Models to extract semantic relationships between abstract representations of system capabilities. These relations, which are often assumed implicitly by non-technical stakeholders (e.g., based on common sense or domain knowledge), are then used to enrich the automated reasoning techniques for eliciting and analyzing the consistency of normative requirements. We show the effectiveness of our approach to normative requirements elicitation and operationalization through a range of real-world case studies.