Abstract
Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models.

Abstract
Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.

Abstract
Traditional recurrent neural networks operate as passive observers of data, unable to modify the information they learn from despite errors that may arise from suboptimal input representations. We introduce Read & Write LSTM (read-write LSTM), a new variant within the family of read & write machine learning (RW-ML) architectures that address this fundamental limitation by integrating input modification directly into the backpropagation process. Read-write LSTM establishes a dynamic feedback loop where input representations evolve alongside model weights through gradient transformation mechanisms. Our approach introduces a principled gradient scaling framework with an adaptive correction rate that carefully controls the extent of data modification, preserving data integrity while enhancing representational power. We comprehensively evaluate read-write LSTM against traditional LSTMs and state-of-the-art transformer models on the M4 competition and Numenta Anomaly Benchmark datasets, demonstrating significant improvements in forecasting accuracy. Notably, read-write LSTM consistently out-performs standard LSTM models in over 70% of time series with complex patterns and achieves superior performance on 55% of anomaly-rich datasets. Through extensive experimentation and analysis, we establish both the theoretical foundations and practical benefits of integrating data modification with neural computation, paving the way for a new generation of adaptive learning systems that actively reshape their inputs rather than merely adapting to them.

Abstract
This paper introduces a novel service-oriented framework, Radio Propagation as a Service (RPaaS), that bridges the gap between raw sensor data and high-fidelity wireless channel simulations. RPaaS transforms noisy, sensor-derived point clouds into accurate 3D models through robust registration, segmentation, and edge detection. These models then feed into a GPU-accelerated ray tracing engine that computes multipath propagation effects, while a separate module derives key electromagnetic and channel parameters. All components are orchestrated via a REST API in a Dockerized environment, enabling dynamic reconfiguration based on sensor data conditions. Experimental validation against commercial ray tracing tools and channel measurements demonstrates that our approach provides accurate simulations even in the presence of sensor noise. © 2025 European Signal Processing Conference, EUSIPCO. All rights reserved.

Abstract
Inductively coupled plasma-mass spectrometry analysis was conducted to examine the geochemical composition of Kfeldspars from various aplite-pegmatites in the Barroso-Alv & atilde;o field, focusing on the differences between Li-rich and Li-barren aplite-pegmatites. The study revealed significant variations in the concentrations of minor and trace elements (Rb, Tl, Li, Ga, Pb, Cs, Ba, Be, Ta, and Sn) present in the K-feldspars of Li-barren, spodumene-rich, and petalite-rich aplite-pegmatites. The data also indicate a geographical trend in both mineralogy and geochemistry across the aplite-pegmatites of the Barroso-Alv & atilde;o field. Li-barren aplite-pegmatites are more concentrated in the southeast, spodumene-rich dominate the center, and petalite-rich varieties are more common in the northwest. Additionally, portable X-ray fluorescence analysis was performed on the crystals of the same samples to evaluate the feasibility of in situ geochemical analysis of K-feldspars, aiming to determine whether an aplite-pegmatite can be quickly identified as Li-rich. This approach seeks to provide a rapid field assessment of whether an aplite-pegmatite justifies further exploration for Li mining. Notably, the trace amounts of Li, Sn, P, and Ta found in K-feldspars are likely due to mineral inclusions of spodumene, cassiterite, apatite, and columbite-tantalite minerals, as observed petrographically in one of these Li-rich aplite-pegmatites.

Abstract
Joint Communication and Sensing (JCAS) systems are emerging as a core technology for next-generation wireless systems due to the potential to achieve higher spectral efficiency, energy savings, and new services beyond communications. This paper provides a review of the state-of-the-art in JCAS systems by focusing on obtrusive passive sensing capabilities and inherent security and privacy challenges that arise from the integration of communication and sensing. From this point of view, we discuss existing techniques for mitigating security and privacy issues, as well as important aspects for the designing of secure and privacy-aware JCAS systems. Additionally, we discuss future research directions by emphasizing on new enabling technologies and their integration on JCAS systems along with their role in privacy and security aspects. We also discuss the required modifications to existing systems and the design of new systems with privacy and security awareness, where the challenging trade-offs between security, privacy and performance of the JCAS system must be considered.