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Clog is a declarative language for describing static code checkers for C. Clog is a dialect of Datalog and adds syntactic pattern matching over the C language. We have built Clog using the MetaDL framework and the Clang C compiler frontend. The MetaDL framework supports Datalog evaluation and syntactic patterns, while the Clang frontend provides AST facts and an AST matching mechanism.We provide t
This study presents a measurement campaign with an ultra-large-scale antenna array in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) outdoor scenarios. Measurements were conducted at a center frequency of 15 GHz with a bandwidth of 4 GHz. A virtual 40×40 planar antenna array, formed by moving a vertically-polarized bi-conical omni-directional antenna (ODA) along regularly spaced grids, was
Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a−1, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality an
The number of users and the information transmitted over wireless networks have been growing constantly during the last decades. Nowadays, the pace of this growth is extremely sharp because of the new applications which heavily rely on wireless networks to meet users' demands. Wireless networks infrastructures are constantly developing to meet these demands. Massive multiple-input multiple-output
With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for preoperative lymph node metastasis prediction. Mammograms and clinicopathological data from 1265 cN0 T1-T2 breast cancer patients (primary surge
Proximity of user has been established as a major cause of performance degradation in multiple-input multiple-output (MIMO) terminals. Here, we investigate the potential of adaptive impedance matching (AIM) to improve the performance of two MIMO terminals in a free space (FS) and a two-hand (TH) user scenario, assuming uniform 3D angular power spectrum for the incoming signal. The results show tha
Background: Predicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be
Programming is a cognitively demanding exercise. In particular, today’s software development requires a collective effort of programmers and the orchestration of a complex programming infrastructure. As disruptive technologies emerge, e.g., AI and quantum computing, the programming practice is undergoing a change, facing an uncertain future that we may not be able to accurately predict but can env
In this paper we present the concept for a teleoperation system for semi-autonomous robotic boats using virtual reality. This system can be used for monitoring autonomous driving as well as for direct manual control. The integration of live sensor data is possible as well as the integration of past measurement results and their correct registration within the virtual representation. Initial field
In this paper, we investigate a class of braided convolutional codes (BCCs), where the component codes are convolutional self-orthogonal codes (CSOCs), called braided convolutional self-orthogonal codes. Compared to conventional BCCs, the advantages of braided CSOCs include the availability of several low-complexity decoding methods and the relative ease of extending these methods to high rates Mo
Dataflow is a Model of Computation (MoC) that describes applications as networks of actors. The CAL Actor Language (CAL) is one of the programming languages for describing such actors. A downside to CAL is that the actors and their networks are rigidly defined - it is not possible to have a parametric number of ports or actions in an actor. This makes it difficult to define flexible applications o
Reconfigurable intelligent surfaces (RISs) are promising to improve energy efficiency and coverage for 6G [1]. In this paper, we aim to optimize the deployment of BSs and RISs for enhanced coverage in terms of received power. Specifically, an active RIS structure [2] with tuneable power amplification is applied, and a framework of mixed integer linear programming (MILP) is proposed for the optimiz
Static analysis tools play a crucial role in software development by detecting bugs and vulnerabilities. However, running these tools separately from the code editing process often causes developers to switch contexts, which can reduce productivity. Previous work has shown how Reference Attribute Grammars (RAGs) can be used for declarative implementation of competitive tooling for intraprocedural
Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework enables the calculation of minimum safe inter-vehicular distances for arbitrary ego vehicle control policies in a computationally efficient manner. We use this framework to enhance and generalize the Respons
The success in the adoption of autonomous vehicles is dependent on their ability to solve rarely occurring safety-critical corner cases. Vehicular communications (V2X) aim at improving safety and efficiency of autonomous driving by adding the capability of explicit inter-vehicular information exchange. We argue that V2X enables another important function, namely the support of ethical driving deci
Static code checkers are tools that help software engineers by automatically finding defects without executing the programs. These tools contain a set of detectors that rely on static program analyses to find common programming defects or to enforce coding guidelines.While existing code checker frameworks package a rich collection of detectors, aimed at common bug defects, the effort to adapt thes
Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function's optimum and are often considered fundamentally distinct from EI. In this work, we challenge this prevailing perspective
Recent work reported that simple Bayesian optimization methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. This paper investigates the 'why'. We identify fundamental challenges that arise in high-dimensional Bayesian optimization and explain why recent methods succeed. Our analysis shows that vanishing gradients caused by Gaussian pr
Using antenna arrays for direction of arrival (DoA) estimation and source localization is a well-researched topic. In this paper, we analyze virtual antenna arrays for DoA estimation where the antenna array geometry is acquired using data from a low-cost inertial measurement unit (IMU). Performance evaluation of an unaided inertial navigation system with respect to individual IMU sensor noise para
