Abstract

Abstract
Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.

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[No abstract available]

Abstract
Context. Inferring the likely formation channel of giant exoplanets and brown dwarf companions from orbital and atmospheric observables remains a formidable challenge. Further and more precise directly measured dynamical masses of these companions are required to inform and gauge formation, evolutionary, and atmospheric models. We present an updated study of the recently discovered companion to HIP 99770 based on observations conducted with the near-infrared interferometer VLTI/GRAVITY.Aims. Through renewed orbital and spectral analyses based on the GRAVITY data, we characterise HIP 99770 b to better constrain its orbit, dynamical mass, and atmospheric properties, as well as to shed light on its likely formation channel.Methods. Upon inclusion of the new high-precision astrometry epoch, we ran an orbit fit to further constrain the dynamical mass of the companion and the orbit solution. We also analysed the GRAVITY K-band spectrum, placing it into context with literature data, and extracting magnitude, age, spectral type, bulk properties and atmospheric characteristics of HIP 99770 b.Results. We detected the companion at a radial separation of 417 mas from its host. The new orbit fit yields a dynamical mass of 17-5+6 MJup and an eccentricity of 0.31-0.12+0.06. We also find that additional relative astrometry epochs in the future will not enable further constraints on the dynamical mass due to the dominating relative uncertainty on the Hipparcos-Gaia proper motion anomaly that is used in the orbit-fitting routine. The publication of Gaia DR4 will likely ease this predicament. Based on the spectral analysis, we find that the companion is consistent with spectral type L8 and exhibits a potential metal enrichment in its atmosphere. Adopting the AMES-DUSTY model to infer its age, within its dynamical mass constraint the companion conceivably corresponds to either a younger (28-14+15 Myr) object with a mass just below the deuterium-burning limit or an older (119-10+37 Myr) body with a mass just above the deuterium-burning limit.Conclusions. These results do not yet allow for a definite inference of the companion's formation channel. Nevertheless, the new constraints on its bulk properties and the additional GRAVITY spectrum presented here will aid future efforts to determine the formation history of HIP 99770 b.

Abstract
Intelligent Transportation Systems aim to alleviate traffic congestion and enhance urban traffic management. Transformer-based methods have shown promise in traffic prediction due to their capability to handle long-range dependencies. However, they disregard local context during parallel processing and can be computationally expensive for large traffic networks. On the other hand, they miss the hierarchical information hidden in regions of large traffic networks. To address these issues, we introduce CSCN, a novel framework that clusters traffic sensors based on data similarity, employs clustered multi-head self-attention for efficient hierarchical pattern learning, and utilizes causal convolutional attention for capturing local temporal trends. In addition to these advancements, we integrate snapshot ensemble learning into CSCN, allowing for the exploitation of diverse snapshots obtained during training to enrich predictive performance. Evaluations of real-world data highlight CSCN's superiority in traffic flow prediction, showcasing its potential for enhancing transportation systems with improved accuracy and efficiency.

Abstract
For seven years, the Text2Story Workshop series has fostered a vibrant community dedicated to understanding narrative structure in text, resulting in significant contributions to the field and developing a shared understanding of the challenges in this domain. While traditional methods have yielded valuable insights, the advent of Transformers and LLMs have ignited a new wave of interest in narrative understanding. The previous iteration of the workshop also witnessed a surge in LLM-based approaches, demonstrating the community’s growing recognition of their potential. In this eighth edition we propose to go deeper into the role of LLMs in narrative understanding. While LLMs have revolutionized the field of NLP and are the go-to tools for any NLP task, the ability to capture, represent and analyze contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. Consequently, this iteration of the workshop will explore the issues involved in using LLMs to unravel narrative structures, while also examining the characteristics of narratives generated by LLMs. By fostering dialogue on these emerging areas, we aim to continue the workshop's tradition of driving innovation in narrative understanding research. Text2Story encompasses sessions covering full research papers, work-in-progress, demos, resources, position and dissemination papers, along with one keynote talk.