The higher-order correlation clustering problem for a graph $G$ and costs associated with cliques of $G$ consists in finding a clustering of $G$ so as to minimize the sum of the costs of those cliques whose nodes all belong to the same cluster. To tackle this NP-hard problem in practice, local search heuristics have been proposed and studied in the context of applications. Here, we establish partial optimality conditions for cubic correlation clustering, i.e., for the special case of at most 3-cliques. We define and implement algorithms for deciding these conditions and examine their effectiveness numerically, on two data sets.

Accurate arrival time prediction (ATP) of buses and trams plays a crucial role in public transport operations. Current methods focused on modeling one-dimensional temporal information but overlooked the latent periodic information within time series. Moreover, most studies developed algorithms for ATP based on a single or a few routes of public transport, which reduces the transferability of the prediction models and their applicability in public transport management systems. To this end, this paper proposes \textit{ArrivalNet}, a two-dimensional temporal variation-based multi-step ATP for buses and trams. It decomposes the one-dimensional temporal sequence into intra-periodic and inter-periodic variations, which can be recast into two-dimensional tensors (2D blocks). Each row of a tensor contains the time points within a period, and each column involves the time points at the same intra-periodic index across various periods. The transformed 2D blocks in different frequencies have an image-like feature representation that enables effective learning with computer vision backbones (e.g., convolutional neural network). Drawing on the concept of residual neural network, the 2D block module is designed as a basic module for flexible aggregation. Meanwhile, contextual factors like workdays, peak hours, and intersections, are also utilized in the augmented feature representation to improve the performance of prediction. 125 days of public transport data from Dresden were collected for model training and validation. Experimental results show that the root mean square error, mean absolute error, and mean absolute percentage error of the proposed predictor decrease by at least 6.1\%, 14.7\%, and 34.2\% compared with state-of-the-art baseline methods.

She's not long on charisma or passion but keeps perfect rhythm and is never prone to temperamental outbursts against the musicians beneath her three batons. Meet MAiRA Pro S, the next-generation robot conductor who made her debut this weekend in Dresden. Her two performances in the eastern German city are intended to show off the latest advances in machine maestros, as well as music written explicitly to harness 21st-century technology. The artistic director of Dresden's Sinfoniker, Markus Rindt, said the intention was "not to replace human beings" but to perform complex music that human conductors would find impossible. The Sinfoniker, long known for innovation and political statements, is celebrating its 25th anniversary with the Robotersinfonie at the Hellerau hall in a concert divided into two parts, one purely human and, after the interval, one that is robot-led.

Modern hardware designs have grown increasingly efficient and complex. However, they are often susceptible to Common Weakness Enumerations (CWEs). This paper is focused on the formal verification of CWEs in a dataset of hardware designs written in SystemVerilog from Regenerative Artificial Intelligence (AI) powered by Large Language Models (LLMs). We applied formal verification to categorize each hardware design as vulnerable or CWE-free. This dataset was generated by 4 different LLMs and features a unique set of designs for each of the 10 CWEs we target in our paper. We have associated the identified vulnerabilities with CWE numbers for a dataset of 60,000 generated SystemVerilog Register Transfer Level (RTL) code. It was also found that most LLMs are not aware of any hardware CWEs; hence they are usually not considered when generating the hardware code. Our study reveals that approximately 60% of the hardware designs generated by LLMs are prone to CWEs, posing potential safety and security risks. The dataset could be ideal for training LLMs and Machine Learning (ML) algorithms to abstain from generating CWE-prone hardware designs. With the increasing complexity of project requirements, hardware designs have also evolved in a similar way. Modern System-on-Chip (SoC) designs are very complex and often require smart methodologies to address simple problems.

Monitoring the status of large computing systems is essential to identify unexpected behavior and improve their performance and uptime. However, due to the large-scale and distributed design of such computing systems as well as a large number of monitoring parameters, automated monitoring methods should be applied. Such automatic monitoring methods should also have the ability to adapt themselves to the continuous changes in the computing system. In addition, they should be able to identify behavioral anomalies in useful time, to perform appropriate reactions. This work proposes a general lightweight and unsupervised method for near real-time anomaly detection using operational data measurement on large computing systems. The proposed model requires as little as 4 hours of data and 50 epochs for each training process to accurately resemble the behavioral pattern of computing systems.

Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location.

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Despite its small size, this soft robot can manoeuvre on solid ground and through water (pictured). A pea-sized origami robot can fold, unfold and perform a range of acrobatic moves -- potentially making it useful for many biomedical applications1. All prices are NET prices. VAT will be added later in the checkout. Tax calculation will be finalised during checkout.

The first two generations of cellular--1G/2G--enabled ubiquitous voice connectivity. Even generations introduced services for business customers, and odd generations democratized them for consumers. One main avenue for achieving this is cost reduction.6 Another avenue is radio access with joint communications and sensing.7 New services are envisioned, such as low-altitude air traffic control, detecting, for example, bird migration and adapting drone services accordingly. Every opportunity of improving sensing is an opportunity for spying.

Social bots - machines that interact as social partners for us humans - are increasingly encountered in everyday life. So far, they mainly appear as software robots in social media, where they like, retweet, but also text and comment. Therefore, they have natural language capabilities and can even communicate synchronously with users as chatbots. However, what happens when they leave the cyber world and suddenly appear face to face with us? How does this affect us as interaction partners and how will our interactions with the machines develop?