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Vehicle-to-everything (V2X) networks support a variety of safety, entertainment, and commercial applications. This is realized by applying the principles of the Internet of Vehicles (IoV) to facilitate connectivity among vehicles and between vehicles and roadside units (RSUs). Network congestion management is essential for IoVs and it represents a significant concern due to its impact on improving the efficiency of transportation systems and providing reliable communication among vehicles for the timely delivery of safety-critical packets. This paper introduces a framework for proactive congestion management for IoV networks. We generate congestion scenarios and a data set to predict the congestion using LSTM. We present the framework and the packet congestion dataset. Simulation results using SUMO with NS3 demonstrate the effectiveness of the framework for forecasting IoV network congestion and clustering/prioritizing packets employing recurrent neural networks., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024

The rapid evolution of 5G and beyond has advanced space-air-terrestrial networks, with unmanned aerial vehicles (UAVs) offering enhanced coverage, flexible configurations, and cost efficiency. However, deploying UAV-based systems presents challenges including varying propagation conditions and hardware limitations. While simulators and theoretical models have been developed, real-world experimentation is critically important to validate the research. Digital twins, virtual replicas of physical systems, enable emulation that bridge theory and practice. This paper presents our experimental results from AERPAW's digital twin, showcasing its ability to simulate UAV communication scenarios and providing insights into system performance and reliability., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024--UAV Communication and Experimentation Workshop

With the rise of 5G and open radio access networks (O-RAN), there is a growing demand for customizable experimental platforms dedicated to security testing, as existing testbeds do not prioritize this area. Traditional, hardware-dependent testing methods pose challenges for smaller companies and research institutions. The growing wireless threat landscape highlights the critical need for proactive security testing, as 5G and O-RAN deployments are appealing targets for cybercriminals. To address these challenges, this article introduces the Soft Tester UE (soft T-UE), a software-defined test equipment designed to evaluate the security of 5G and O-RAN deployments via the Uu air interface between the user equipment (UE) and the network. The outcome is to deliver a free, open-source, and expandable test instrument to address the need for both standardized and customizable automated security testing. By extending beyond traditional security metrics, the soft T-UE promotes the development of new security measures and enhances the capability to anticipate and mitigate potential security breaches. The tool's automated testing capabilities are demonstrated through a scenario where the Radio Access Network (RAN) under test is evaluated when it receives fuzzed data when initiating a connection with an UE., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024--RitiRAN Workshop

Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precision assembly tasks remains a significant challenge. This paper presents a novel framework that utilizes diffusion models to generate 6D wrench for high-precision tactile robotic insertion tasks. It learns from demonstrations performed on a single task and achieves a zero-shot transfer success rate of 95.7% across various novel high-precision tasks. Our method effectively inherits the self-adaptability demonstrated by our previous work. In this framework, we address the frequency misalignment between the diffusion policy and the real-time control loop with a dynamic system-based filter, significantly improving the task success rate by 9.15%. Furthermore, we provide a practical guideline regarding the trade-off between diffusion models' inference ability and speed., Comment: 7 pages

Robotic assembly tasks are open challenges due to the long task horizon and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for their modularity and flexibility, but manually designing them can be effort-intensive. Large language models (LLMs) have recently been applied in robotic task planning for generating action sequences, but their ability to generate BTs has not been fully investigated. To this end, We propose LLM as BT-planner, a novel framework to leverage LLMs for BT generation in robotic assembly task planning and execution. Four in-context learning methods are introduced to utilize the natural language processing and inference capabilities of LLMs to produce task plans in BT format, reducing manual effort and ensuring robustness and comprehensibility. We also evaluate the performance of fine-tuned, fewer-parameter LLMs on the same tasks. Experiments in simulated and real-world settings show that our framework enhances LLMs' performance in BT generation, improving success rates in BT generation through in-context learning and supervised fine-tuning., Comment: 8 pages

This study utilizes mathematical modeling to offer computational insights into biological heat transfer within elastic skin tissues. This heat transfer is driven by a moving heat source that relies on various relaxation times. Time delay parameters were incorporated based on these relaxation times, highlighting the importance of understanding heat transfer mechanisms and thermo-mechanical interactions to ensure the effectiveness of thermal treatment procedures. Given the significant impact of different variables on thermal relaxation times, their effects were analyzed on the distributions of displacement, thermal stress, and temperature in living tissues. By applying advanced mathematical techniques such as Laplace transforms, precise evaluations of these distributions were achieved. The results are presented graphically, with a focus on enhancing treatment outcomes by deepening our understanding of the behavior of living tissues in thermal environments., Comment: Some of the results contain errors due to mistakes in the equations

We explore how higher-order derivative terms impact a physical observable within a Lorentz-Symmetry Violating (LSV) framework. We specifically examine Podolsky electrodynamics coupled with the Carroll-Field-Jackiw model (CFJ). Our analysis shows how vacuum and wave propagation are affected by the interaction of LSV parameters with properties of Podolsky electrodynamics. In this scenario, our results indicate that the new electromagnetic vacuum is birefringent, dichroic, and dispersive. We also point out that the higher-derivative term in the presence of the CFJ space-time anisotropy induces an oscillation mechanism between the photon, which acquires a non-vanishing mass, and the massive vector mode carried by the higher derivative. We then investigate the electromagnetic radiation emitted by a moving charged particle interacting with this new medium. Our analysis reveals that the refractive vacuum through which the charged particle travels influences the electromagnetic radiation, leading to a profile similar to the Cherenkov effect., Comment: 13 pages

Non-prehensile object transportation offers a way to enhance robotic performance in object manipulation tasks, especially with unstable objects. Effective trajectory planning requires simultaneous consideration of robot motion constraints and object stability. Here, we introduce a physical model for object stability and propose a novel trajectory planning approach for non-prehensile transportation along arbitrary straight lines in 3D space. Validation with a 7-DoF Franka Panda robot confirms improved transportation speed via tray rotation integration while ensuring object stability and robot motion constraints., Comment: Accepted to the European Robotic Forum (ERF) 2024

Routine computed tomography (CT) scans often detect a wide range of renal cysts, some of which may be malignant. Early and precise localization of these cysts can significantly aid quantitative image analysis. Current segmentation methods, however, do not offer sufficient interpretability at the feature and pixel levels, emphasizing the necessity for an explainable framework that can detect and rectify model inaccuracies. We developed an interpretable segmentation framework and validated it on a multi-centric dataset. A Variational Autoencoder Generative Adversarial Network (VAE-GAN) was employed to learn the latent representation of 3D input patches and reconstruct input images. Modifications in the latent representation using the gradient of the segmentation model generated counterfactual explanations for varying dice similarity coefficients (DSC). Radiomics features extracted from these counterfactual images, using a ground truth cyst mask, were analyzed to determine their correlation with segmentation performance. The DSCs for the original and VAE-GAN reconstructed images for counterfactual image generation showed no significant differences. Counterfactual explanations highlighted how variations in cyst image features influence segmentation outcomes and showed model discrepancies. Radiomics features correlating positively and negatively with dice scores were identified. The uncertainty of the predicted segmentation masks was estimated using posterior sampling of the weight space. The combination of counterfactual explanations and uncertainty maps provided a deeper understanding of the image features within the segmented renal cysts that lead to high uncertainty. The proposed segmentation framework not only achieved high segmentation accuracy but also increased interpretability regarding how image features impact segmentation performance.

Recent research has seen notable progress in the development of linkage-based artificial hands. While previous designs have focused on adaptive grasping, dexterity and biomimetic artificial skin, only a few systems have proposed a lightweight, accessible solution integrating tactile sensing with a compliant linkage-based mechanism. This paper introduces OPENGRASP LITE, an open-source, highly integrated, tactile, and lightweight artificial hand. Leveraging compliant linkage systems and MEMS barometer-based tactile sensing, it offers versatile grasping capabilities with six degrees of actuation. By providing tactile sensors and enabling soft grasping, it serves as an accessible platform for further research in tactile artificial hands., Comment: Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 October 2024

Traditional sperm morphology analysis is based on tedious manual annotation. Automated morphology analysis of a high number of sperm requires accurate segmentation of each sperm part and quantitative morphology evaluation. State-of-the-art instance-aware part segmentation networks follow a "detect-then-segment" paradigm. However, due to sperm's slim shape, their segmentation suffers from large context loss and feature distortion due to bounding box cropping and resizing during ROI Align. Moreover, morphology measurement of sperm tail is demanding because of the long and curved shape and its uneven width. This paper presents automated techniques to measure sperm morphology parameters automatically and quantitatively. A novel attention-based instance-aware part segmentation network is designed to reconstruct lost contexts outside bounding boxes and to fix distorted features, by refining preliminary segmented masks through merging features extracted by feature pyramid network. An automated centerline-based tail morphology measurement method is also proposed, in which an outlier filtering method and endpoint detection algorithm are designed to accurately reconstruct tail endpoints. Experimental results demonstrate that the proposed network outperformed the state-of-the-art top-down RP-R-CNN by 9.2% [AP]_vol^p, and the proposed automated tail morphology measurement method achieved high measurement accuracies of 95.34%,96.39%,91.2% for length, width and curvature, respectively., Comment: Accepted to ICRA 2024

A drawing robot avatar is a robotic system that allows for telepresence-based drawing, enabling users to remotely control a robotic arm and create drawings in real-time from a remote location. The proposed control framework aims to improve bimanual robot telepresence quality by reducing the user workload and required prior knowledge through the automation of secondary or auxiliary tasks. The introduced novel method calculates the near-optimal Cartesian end-effector pose in terms of visual feedback quality for the attached eye-to-hand camera with motion constraints in consideration. The effectiveness is demonstrated by conducting user studies of drawing reference shapes using the implemented robot avatar compared to stationary and teleoperated camera pose conditions. Our results demonstrate that the proposed control framework offers improved visual feedback quality and drawing performance., Comment: Accepted to ICRA 2024

Many systems occurring in real-world applications, such as controlling the motions of robots or modeling the spread of diseases, are switched impulsive systems. To ensure that the system state stays in a safe region (e.g., to avoid collisions with obstacles), barrier functions are widely utilized. As the system dimension increases, deriving suitable barrier functions becomes extremely complex. Fortunately, many systems consist of multiple subsystems, such as different areas where the disease occurs. In this work, we present sufficient conditions for interconnected switched impulsive systems to maintain safety by constructing local barrier functions for the individual subsystems instead of a global one, allowing for much easier and more efficient derivation. To validate our results, we numerically demonstrate its effectiveness using an epidemiological model., Comment: Accepted for publication in the proceedings of the IEEE 63rd Conference on Decision and Control

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

We study the optimization landscape of a smooth nonconvex program arising from synchronization over the two-element group $\mathbf{Z}_2$, that is, recovering $z_1, \dots, z_n \in \{\pm 1\}$ from (noisy) relative measurements $R_{ij} \approx z_i z_j$. Starting from a max-cut--like combinatorial problem, for integer parameter $r \geq 2$, the nonconvex problem we study can be viewed both as a rank-$r$ Burer--Monteiro factorization of the standard max-cut semidefinite relaxation and as a relaxation of $\{ \pm 1 \}$ to the unit sphere in $\mathbf{R}^r$. First, we present deterministic, non-asymptotic conditions on the measurement graph and noise under which every second-order critical point of the nonconvex problem yields exact recovery of the ground truth. Then, via probabilistic analysis, we obtain asymptotic guarantees for three benchmark problems: (1) synchronization with a complete graph and Gaussian noise, (2) synchronization with an Erd\H{o}s--R\'enyi random graph and Bernoulli noise, and (3) graph clustering under the binary symmetric stochastic block model. In each case, we have, asymptotically as the problem size goes to infinity, a benign nonconvex landscape near a previously-established optimal threshold for exact recovery; we can approach this threshold to arbitrary precision with large enough (but finite) rank parameter $r$. In addition, our results are robust to monotone adversaries.

Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capable of highly dynamic motions even with limited motor power. However, the time-dependency of this energy storage and release mechanism remains one of the major challenges in controlling elastic robots. A possible remedy is the introduction of locking elements (i.e. clutches and brakes) in the drive train. This gives rise to a new class of robots, so-called clutched-elastic robots (CER), with which it is possible to precisely control the energy-transfer timing. A prevalent challenge in the realm of CERs is the automatic discovery of clutch sequences. Due to complexity, many methods still rely on pre-defined modes. In this paper, we introduce a novel contact-implicit scheme designed to optimize both control input and clutch sequence simultaneously. A penalty in the objective function ensures the prevention of unnecessary clutch transitions. We empirically demonstrate the effectiveness of our proposed method on a double pendulum equipped with two of our newly proposed clutch-based Bi-Stiffness Actuators (BSA)., Comment: Accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024). The first two authors contributed equally to this work. A cited paper, "Posa M, Cantu C, and Tedrake R (2014)" has a corrigendum (https://doi.org/10.1177/0278364914533878) referencing earlier work by Dr. Kerim Yunt

We demonstrate a compact ion beam device capable of accelerating H$^+$ and D$^+$ ions up to 75keV energy, on to a solid target, with sufficient beam current to study fusion reactions. The ion beam system uses a microwave driven plasma source to generate ions that are accelerated to high energy with a direct current (DC) acceleration structure. The plasma source is driven by pulsed microwaves from a solid-state radiofrequency (RF) amplifier, which is impedance matched to the plasma source chamber at the ISM band frequency (2.4-2.5GHz). The plasma chamber is held at high positive DC potential and is isolated from the impedance matching structure (at ground potential) by a dielectric-filled gap. To facilitate the use of high-energy-particle detectors near the target, the plasma chamber is biased to a high positive voltage, while the target remains grounded. A target loaded with deuterium is used to study D-D fusion and a B$_4$C or LaB$_6$ target is used to study p-$^{11}$B fusion. Detectors include solid-state charged particle detector and a scintillation fast neutron detector. The complete ion beam system can fit on a laboratory table and is a useful tool for teaching undergraduate and graduate students about the physics of fusion., Comment: 21 pages, 13 figures, accepted manuscript in Physics Open (ISSN: 2666-0326)

Photonic Neural Networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementation when compared to electronics, such as the need to represent input features in the photonic domain before feeding them into the network. In this encoding process, it is common to combine multiple features into a single input to reduce the number of inputs and associated devices, leading to smaller and more energy-efficient PNNs. Although this alters the network's handling of input data, its impact on PNNs remains understudied. This paper addresses this open question, investigating the effect of commonly used encoding strategies that combine features on the performance and learning capabilities of PNNs. Here, using the concept of feature importance, we develop a mathematical methodology for analyzing feature combination. Through this methodology, we demonstrate that encoding multiple features together in a single input determines their relative importance, thus limiting the network's ability to learn from the data. Given some prior knowledge of the data, however, this can also be leveraged for higher accuracy. By selecting an optimal encoding method, we achieve up to a 12.3% improvement in accuracy of PNNs trained on the Iris dataset compared to other encoding techniques, surpassing the performance of networks where features are not combined. These findings highlight the importance of carefully choosing the encoding to the accuracy and decision-making strategies of PNNs, particularly in size or power constrained applications.

The recent detection of gamma-ray burst GRB~221009A has attracted attention due to its record brightness and first-ever detection of $\gtrsim 10$ TeV $\gamma$ rays from a GRB. Despite being the second-nearest GRB ever detected, at a redshift of $z=0.151$, the distance is large enough for severe attenuation of $\gamma$-ray flux at these energies due to $\gamma\gamma\to e^\pm$ pair production with the extragalactic background light (EBL). Here, we investigate whether the presence of cosmic voids along the line of sight can significantly impact the detectability of very-high energy (VHE, $>$ 100 GeV) gamma rays from distant sources. Notably, we find that the gamma-gamma opacity for VHE gamma rays can be reduced by approximately 10\% and up to 30\% at around 13 TeV, the highest-energy photon detected from GRB~221009A, for intervening cosmic voids along the line-of-sight with a combined radius of 110 Mpc, typically found from voids catalogs, and 250 Mpc, respectively. This reduction is substantially higher for TeV photons compared to GeV photons, attributable to the broader target photon spectrum that TeV photons interact with. This finding implies that VHE photons are more susceptible to variations in the EBL spectrum, especially in regions dominated by cosmic voids. Our study sheds light on the detection of $\gtrsim 10$ TeV gamma rays from GRB 221009A in particular, and on the detection of extragalactic VHE sources in general., Comment: 11 pages, 10 figures, Accepted for publication in MNRAS

In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions. While several functions have been developed and derived over the past decades, little information is available on the mathematical and visual description, range of suitability, and applications of many such functions. To bridge this knowledge gap, this review provides an exhaustive survey of more than 300 benchmark functions used in the evaluation of optimization and metaheuristics algorithms. This review first catalogs benchmark and test functions based on their characteristics, complexity, properties, visuals, and domain implications to offer a wide view that aids in selecting appropriate benchmarks for various algorithmic challenges. This review also lists the 25 most commonly used functions in the open literature and proposes two new, highly dimensional, dynamic and challenging functions that could be used for testing new algorithms. Finally, this review identifies gaps in current benchmarking practices and suggests directions for future research.

Music evokes emotion in many people. We introduce a novel way to manipulate the emotional content of a song using AI tools. Our goal is to achieve the desired emotion while leaving the original melody as intact as possible. For this, we create an interactive pipeline capable of shifting an input song into a diametrically opposed emotion and visualize this result through Russel's Circumplex model. Our approach is a proof-of-concept for Semantic Manipulation of Music, a novel field aimed at modifying the emotional content of existing music. We design a deep learning model able to assess the accuracy of our modifications to key, SoundFont instrumentation, and other musical features. The accuracy of our model is in-line with the current state of the art techniques on the 4Q Emotion dataset. With further refinement, this research may contribute to on-demand custom music generation, the automated remixing of existing work, and music playlists tuned for emotional progression.

We present FaithFill, a diffusion-based inpainting object completion approach for realistic generation of missing object parts. Typically, multiple reference images are needed to achieve such realistic generation, otherwise the generation would not faithfully preserve shape, texture, color, and background. In this work, we propose a pipeline that utilizes only a single input reference image -having varying lighting, background, object pose, and/or viewpoint. The singular reference image is used to generate multiple views of the object to be inpainted. We demonstrate that FaithFill produces faithful generation of the object's missing parts, together with background/scene preservation, from a single reference image. This is demonstrated through standard similarity metrics, human judgement, and GPT evaluation. Our results are presented on the DreamBooth dataset, and a novel proposed dataset.

In the fields of Optical Character Recognition (OCR) and Natural Language Processing (NLP), integrating multilingual capabilities remains a critical challenge, especially when considering languages with complex scripts such as Arabic. This paper introduces the Comprehensive Post-OCR Parsing and Receipt Understanding Dataset (CORU), a novel dataset specifically designed to enhance OCR and information extraction from receipts in multilingual contexts involving Arabic and English. CORU consists of over 20,000 annotated receipts from diverse retail settings, including supermarkets and clothing stores, alongside 30,000 annotated images for OCR that were utilized to recognize each detected line, and 10,000 items annotated for detailed information extraction. These annotations capture essential details such as merchant names, item descriptions, total prices, receipt numbers, and dates. They are structured to support three primary computational tasks: object detection, OCR, and information extraction. We establish the baseline performance for a range of models on CORU to evaluate the effectiveness of traditional methods, like Tesseract OCR, and more advanced neural network-based approaches. These baselines are crucial for processing the complex and noisy document layouts typical of real-world receipts and for advancing the state of automated multilingual document processing. Our datasets are publicly accessible (https://github.com/Update-For-Integrated-Business-AI/CORU).

Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches. A prominent feature of this review is the investigation of core challenges within the VAD paradigms including large-scale datasets, features extraction, learning methods, loss functions, regularization, and anomaly score prediction. Moreover, this review also investigates the vision language models (VLMs) as potent feature extractors for VAD. VLMs integrate visual data with textual descriptions or spoken language from videos, enabling a nuanced understanding of scenes crucial for anomaly detection. By addressing these challenges and proposing future research directions, this review aims to foster the development of robust and efficient VAD systems leveraging the capabilities of VLMs for enhanced anomaly detection in complex real-world scenarios. This comprehensive analysis seeks to bridge existing knowledge gaps, provide researchers with valuable insights, and contribute to shaping the future of VAD research.

We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phenomenon of semantic relatedness across 14 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by the relatively limited availability of NLP resources. Each instance in the datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. Participating systems were asked to rank sentence pairs by their closeness in meaning (i.e., their degree of semantic relatedness) in the 14 languages in three main tracks: (a) supervised, (b) unsupervised, and (c) crosslingual. The task attracted 163 participants. We received 70 submissions in total (across all tasks) from 51 different teams, and 38 system description papers. We report on the best-performing systems as well as the most common and the most effective approaches for the three different tracks., Comment: SemEval 2024 Task Description Paper. arXiv admin note: text overlap with arXiv:2402.08638

In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look similar to answerable ones, along with additional labels of open-domain questions marks a crucial advancement in Arabic NLP resources. We also present AraDPR, the first dense passage retrieval model trained on the Arabic Wikipedia corpus, specifically designed to tackle the unique challenges of Arabic text retrieval. Furthermore, our study includes extensive benchmarking of large language models (LLMs) for Arabic question answering, critically evaluating their performance in the Arabic language context. In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP. The dataset and code are publicly accessible for further research https://github.com/DataScienceUIBK/ArabicaQA., Comment: Accepted at SIGIR 2024

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimization problem can be very complex or extremely conservative when the traditional linear parameter-varying formulation is used. Thus, different from previous approaches in the literature, we propose to use polynomial representations for DSs, which yields an optimization problem that can be tackled by sum-of-squares techniques. Finally, our approach can handle obstacle shapes that fall outside the scope of assumptions typically found in the literature concerning obstacle avoidance within the DS learning framework. Supplementary material can be found at the project webpage: https://martinschonger.github.io/abc-ds, Comment: 7 pages, 7 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security challenges. This paper proposes leveraging zero trust principles for O-RAN security. We introduce zero trust RAN (ZTRAN), which embeds service authentication, intrusion detection, and secure slicing subsystems that are encapsulated as xApps. We implement ZTRAN on the open artificial intelligence cellular (OAIC) research platform and demonstrate its feasibility and effectiveness in terms of legitimate user throughput and latency figures. Our experimental analysis illustrates how ZTRAN's intrusion detection and secure slicing microservices operate effectively and in concert as part of O-RAN Alliance's containerized near-real time RIC. Research directions include exploring machine learning and additional threat intelligence feeds for improving the performance and extending the scope of ZTRAN., Comment: This article has been accepted for publication in the IEEE Wireless Communications Magazine