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There is a growing interest in codebook-based beam-steering for millimeter-wave (mmWave) systems due to its potential for low complexity and rapid beam search. A key focus of recent research has been the design of codebooks that strike a trade-off between achievable gain and codebook size, which directly impacts beam search time. Statistical approaches have shown promise by leveraging the likelihood that certain beam directions (equivalently, sets of phase-shifter configurations) are more probable than others. Such approaches are shown to be valid for static, non-rotating transmission stations such as base stations. However, for the case of user terminals that are constantly changing orientation, the possible phase-shifter configurations become equally probable, rendering statistical methods less relevant. On the other hand, user terminals come with a large number of possible steering vector configurations, which can span up to six orders of magnitude. Therefore, efficient solutions to reduce the codebook size (set of possible steering vectors) without compromising array gain are needed. We address this challenge by proposing a novel and practical codebook refinement technique, aiming to reduce the codebook size while maintaining array gain within $\gamma$ dB of the maximum achievable gain at any random orientation of the user terminal. We project that a steering vector at a given angle could effectively cover adjacent angles with a small gain loss compared to the maximum achievable gain. We demonstrate experimentally that it is possible to reduce the codebook size from $1024^{16}$ to just a few configurations (e.g., less than ten), covering all angles while maintaining the gain within $\gamma=3$ dB of the maximum achievable gain., Comment: 6 pages, 3 figures, conference

Over the past two decades, the Web Ontology Language (OWL) has been instrumental in advancing the development of ontologies and knowledge graphs, providing a structured framework that enhances the semantic integration of data. However, the reliability of deductive reasoning within these systems remains challenging, as evidenced by inconsistencies among popular reasoners in recent competitions. This evidence underscores the limitations of current testing-based methodologies, particularly in high-stakes domains such as healthcare. To mitigate these issues, in this paper, we have developed VEL, a formally verified EL++ reasoner equipped with machine-checkable correctness proofs that ensure the validity of outputs across all possible inputs. This formalization, based on the algorithm of Baader et al., has been transformed into executable OCaml code using the Coq proof assistant's extraction capabilities. Our formalization revealed several errors in the original completeness proofs, which led to changes to the algorithm to ensure its completeness. Our work demonstrates the necessity of mechanization of reasoning algorithms to ensure their correctness at theoretical and implementation levels.

High-temperature superconductor (HTS) coated conductors (CC) are often wound into pancake coils with electrical insulation in-between the turns. The copper terminals are used for current injection and conduction cooling. An inherent variation of the critical current along the CC length results from its manufacturing process. This variation causes non-uniform heat generation, particularly when the coil is operated at a high fraction of the nominal critical current or when large critical current defects are present. The temperature distribution resulting from the balance between cooling and heating, in combination with the magnetic field and critical current distributions, determines whether a thermal runaway occurs. Accurately predicting the level of critical current defects that can be tolerated during conduction-cooled operation is difficult and requires a 3D coupled electromagnetic and thermal simulation. This paper presents the results of simulations that are performed with the open-source Finite Element Quench Simulator (FiQuS) tool developed at CERN as part of the STEAM framework. The 3D coupled magnetodynamic-thermal simulations are based on the H-phi formulation and use thin shell approximations, a CC homogenization and conduction-cooling. The critical current (Ic) is varied along the CC length. The effect of a single defect specified as a reduction of Ic along the CC length is investigated in terms of the coil's ability to reach and maintain the operating conditions. The Ic and length of the defect that results in a thermal runaway are analyzed in terms of defect location. In addition, a classical 1D scenario with a quench heater is studied. Both the local defect and the heater cases are compared in terms of the voltage signal available for quench detection. These cases result in very different requirements for quench detection, and their implications are discussed., Comment: This work has been presented at the Applied Superconductivity Conference 2024 (ASC 2024). This version of the manuscript has been submitted to the ASC 2024 proceedings in the IEEE Transactions on Applied Superconductivity Special Issue for ASC'24 for possible publication

This work presents Mamba Imitation Learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies. MaIL leverages Mamba, a state-space model designed to selectively focus on key features of the data. While Transformers are highly effective in data-rich environments due to their dense attention mechanisms, they can struggle with smaller datasets, often leading to overfitting or suboptimal representation learning. In contrast, Mamba's architecture enhances representation learning efficiency by focusing on key features and reducing model complexity. This approach mitigates overfitting and enhances generalization, even when working with limited data. Extensive evaluations on the LIBERO benchmark demonstrate that MaIL consistently outperforms Transformers on all LIBERO tasks with limited data and matches their performance when the full dataset is available. Additionally, MaIL's effectiveness is validated through its superior performance in three real robot experiments. Our code is available at https://github.com/ALRhub/MaIL.

Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using Computed Tomography Pulmonary Angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE mortality. Materials and Methods: 918 patients (median age 64 years, range 13-99 years, 52% female) with 3,978 CTPAs were identified via retrospective review across three institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes. Four models were developed as follows: (1) using CTPA imaging features only; (2) using clinical variables only; (3) multimodal, integrating both CTPA and clinical variables; and (4) multimodal fused with calculated PESI score. Performance and contribution from each modality were evaluated using concordance index (c-index) and Net Reclassification Improvement, respectively. Performance was compared to PESI predictions using the Wilcoxon signed-rank test. Kaplan-Meier analysis was performed to stratify patients into high- and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction. Results: For both data sets, the PESI-fused and multimodal models achieved higher c-indices than PESI alone. Following stratification of patients into high- and low-risk groups by multimodal and PESI-fused models, mortality outcomes differed significantly (both p<0.001). A strong correlation was found between high-risk grouping and RV dysfunction. Conclusions: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross LLMs alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologist. Multi-center experiments validate both MRANet's overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies. The code is available at https://github.com/zzs95/MRANet.

The COVID-19 pandemic has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates. This paper introduces an interpretable deep survival prediction model designed specifically for improved understanding and trust in COVID-19 prognosis using chest X-ray (CXR) images. By integrating a large-scale pretrained image encoder, Risk-specific Grad-CAM, and anatomical region detection techniques, our approach produces regional interpretable outcomes that effectively capture essential disease features while focusing on rare but critical abnormal regions. Our model's predictive results provide enhanced clarity and transparency through risk area localization, enabling clinicians to make informed decisions regarding COVID-19 diagnosis with better understanding of prognostic insights. We evaluate the proposed method on a multi-center survival dataset and demonstrate its effectiveness via quantitative and qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and time-dependent AUCs (0.799 and 0.691). These results suggest that our explainable deep survival prediction model surpasses traditional survival analysis methods in risk prediction, improving interpretability for clinical decision making and enhancing AI system trustworthiness.

5G mobile networks leverage Network Function Virtualization (NFV) to offer services in the form of network slices. Each network slice is a logically isolated fragment constructed by service chaining a set of Virtual Network Functions (VNFs). The Network Repository Function (NRF) acts as a central OpenAuthorization (OAuth) 2.0 server to secure inter-VNF communications resulting in a single point of failure. Thus, we propose 5G-WAVE, a decentralized authorization framework for the 5G core by leveraging the WAVE framework and integrating it into the OpenAirInterface (OAI) 5G core. Our design relies on Side-Car Proxies (SCPs) deployed alongside individual VNFs, allowing point-to-point authorization. Each SCP acts as a WAVE engine to create entities and attestations and verify incoming service requests. We measure the authorization latency overhead for VNF registration, 5G Authentication and Key Agreement (AKA), and data session setup and observe that WAVE verification introduces 155ms overhead to HTTP transactions for decentralizing authorization. Additionally, we evaluate the scalability of 5G-WAVE by instantiating more network slices to observe 1.4x increase in latency with 10x growth in network size. We also discuss how 5G-WAVE can significantly reduce the 5G attack surface without using OAuth 2.0 while addressing several key issues of 5G standardization.

Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the emergence of multi-modal data distributions, thereby presenting a formidable challenge for existing imitation learning algorithms. Quantifying a model's capacity to capture and replicate this diversity effectively is still an open problem. In this work, we introduce simulation benchmark environments and the corresponding Datasets with Diverse human Demonstrations for Imitation Learning (D3IL), designed explicitly to evaluate a model's ability to learn multi-modal behavior. Our environments are designed to involve multiple sub-tasks that need to be solved, consider manipulation of multiple objects which increases the diversity of the behavior and can only be solved by policies that rely on closed loop sensory feedback. Other available datasets are missing at least one of these challenging properties. To address the challenge of diversity quantification, we introduce tractable metrics that provide valuable insights into a model's ability to acquire and reproduce diverse behaviors. These metrics offer a practical means to assess the robustness and versatility of imitation learning algorithms. Furthermore, we conduct a thorough evaluation of state-of-the-art methods on the proposed task suite. This evaluation serves as a benchmark for assessing their capability to learn diverse behaviors. Our findings shed light on the effectiveness of these methods in tackling the intricate problem of capturing and generalizing multi-modal human behaviors, offering a valuable reference for the design of future imitation learning algorithms.

Multimodal optimization is often encountered in engineering problems, especially when different and alternative solutions are sought. Evolutionary algorithms can efficiently tackle multimodal optimization thanks to their features such as the concept of population, exploration/exploitation, and being suitable for parallel computation. This paper investigates whether a less-known optimizer, the Big Bang-Big Crunch (BBBC) algorithm, is suitable for multimodal optimization. We extended BBBC and propose k-BBBC, a clustering-based multi-modal optimizer. Additionally, we introduce two post-processing methods to (i) identify the local optima in a set of retrieved solutions (i.e., a population), and (ii) quantify the number of correctly retrieved optima against the expected ones (i.e., success rate). Our results show that k-BBBC performs well even with problems having a large number of optima (tested on $379$ optima) and high dimensionality (tested on $32$ decision variables), but it becomes computationally too expensive for problems with many local optima (i.e., in the CEC'2013 benchmark set). Compared to other multimodal optimization methods, it outperforms them in terms of accuracy (in both search and objective space) and success rate (number of correctly retrieved optima) when tested on basic multimodal functions, especially when elitism is applied; however, it requires knowing the number of optima of a problem, which makes its performance decrease when tested on niching competition test CEC'2013. Lastly, we validated our proposed post-processing methods by comparing their success rate to the actual one: results suggest that these methods can be used to evaluate the performance of a multimodal optimization algorithm by correctly identifying optima and providing an indication of success -- without the need to know where the optima are located in the search space., Comment: 18 pages

The Fifth Generation (5G) mobile core network is designed as a set of Virtual Network Functions (VNFs) hosted on Commercial-Off-the-Shelf (COTS) hardware. This creates a growing demand for general-purpose compute resources as 5G deployments continue to expand. Given their elastic infrastructure, cloud services such as Amazon Web Services (AWS) are attractive platforms to address this need. Therefore, it is crucial to understand the control and user plane Quality of Service (QoS) performance associated with deploying the 5G core on top of a public cloud. To account for both software and communication costs, we build a 5G testbed using open-source components spanning multiple locations within AWS. We present an operational breakdown of the performance overhead for various 5G use cases using different core deployment strategies. Our results indicate that moving specific VNFs into edge regions reduces the latency overhead for key 5G operations. Furthermore, we instantiated multiple user plane connections between availability zones and edge regions with different traffic loads. We observed that the deterioration of connection quality varies depending on traffic loads and is use case specific. Ultimately, our findings provide new insights for Mobile Virtual Network Operators (MVNOs) for optimal placements of their 5G core functions.