ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in a crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from similar to 2000 speakers (cf. similar to 100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community.
In this paper we investigate the use of retrodirective arrays in transponders of ultra high frequency (UHF) radio-frequency identification (RFID) systems and their potential to enhance both the communication range and the energy range of RFID systems. The communication range is increased through the retrodirectivity of the backscatter transponders. An asymmetry in the transponder array is intended to support wireless power transfer (WPT) by focusing the incoming wave onto one rectifier to maximize its efficiency. In this study we use a commercially available integrated circuit (IC) for UHF RFID transponders making the constructed retrodirective transponder compatible with the EPC Gen2 protocol. Theoretical investigations show that through the use of N antennas in the transponder both energy range and communication range can be increased by a factor of root N , corresponding to an increase of about 41% when using two antennas. The realized EPC Gen2-compatible transponder with two antennas achieved an increase of up to 34% in energy range and 38% in communication range. The presented technique is not limited to two antennas and can help to extend the range of backscatter systems without modifications to the reader. This could especially be interesting for applications within the Ambient Internet of Things (A-IoT) of upcoming 6G networks.
The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor of the bHLH/PAS protein family. In this review, we explore the multifaceted roles of AHR in both health and disease, tracing its recognition as a xenobiotic sensor and a central regulator of physiological homeostasis. We begin by recounting six decades of discoveries that have shaped our understanding of AHR, from its canonical function in environmental sensing to its critical roles in development, immune regulation, barrier tissue integrity, and host–microbe interactions. We detail recent structural breakthroughs that have illuminated the ligand-binding mechanisms and regulation of AHR, providing key insights into its activation and transcriptional control. We also highlight the diversity of AHR ligands, ranging from environmental toxins to microbial and dietary metabolites of tryptophan, and their context-dependent effects on AHR activation through the canonical pathway and noncanonical signaling mechanisms. We examine the involvement of AHR in pathologies such as cancer and autoimmune and inflammatory diseases and its potential as a therapeutic target. Finally, emphasis is placed on recent advances and future developments in drug design, aiming to develop modulators with clinical efficacy. This comprehensive synthesis underscores the dual role of AHR as a master integrator of both environmental and endogenous cues. Placing AHR within broader frameworks, such as the exposome, opens new avenues for therapeutic innovation and more effective strategies for disease prevention.
ZusammenfassungIn Kap. 1 haben wir schon VR als innovative Form der Mensch-Computer-Interaktion kennengelernt. In diesem Kapitel behandeln wir die Gestaltung und Realisierung von Interaktionen und der daraus resultierenden Benutzungsschnittstelle (engl. User Interface ) eines VR-Systems detailliert. Ein Nutzer interagiert mit einem VR-System, um virtuelle Objekte auszuwählen (Selektion) und zu verändern (Manipulation) sowie seine Position und seine Blickrichtung in der Virtuellen Umgebung zu bestimmen (Navigation). Dazu kommt die Interaktion mit dem VR-System selbst (Systemsteuerung), um auf einer Metaebene Funktionen außerhalb der Virtuellen Umgebung auszuführen (z. B. das Laden einer neuen Virtuellen Welt). Diese Grundaufgaben der Selektion, Manipulation, Navigation und Systemsteuerung werden in je einem Unterkapitel behandelt. Dabei werden Lösungen für die Realisierung dieser Interaktionen vorgestellt. Wesentlich dabei ist, dass eine gute Usability (dt. Gebrauchstauglichkeit) erreicht wird. Dies ist ein Kern der Mensch-Computer-Interaktion allgemein und wir gehen deshalb auf Grundlagen aus diesem Bereich gleich zu Beginn dieses Kapitels ein. Schließlich betrachten wir in einem Unterkapitel spezielle Entwurfsprozesse, die einen Entwickler bei der Gestaltung und der Realisierung von VR-Interaktionen leiten. Ein wesentlicher Gesichtspunkt dabei ist das wiederholte Validieren der Interaktionen mit Nutzern in Form von Nutzertests. Methoden für die Durchführung und Auswertung von Nutzertests werden daher gesondert im letzten Unterkapitel thematisiert.
Chemical mechanical planarization is an integral part of semiconductor industry. It provides wafersurface smoothing at the nanometer scale and is highly monitored. In a typical industrial fabrication facility, millions of data points are generated annually for process control, fault detection and classification purposes. By leveraging this sensor and process data for the training of machine learning models, a foundation for virtual metrology can be established. Utilizing real-world data from a high-mix, high-volume fab, we developed robust and application-oriented machine learning models capable of predicting non-uniformity and spatially resolved material removal rates on wafer surfaces. Experimental results demonstrate the accuracy of these predictions, with a high degree of precision in estimating spatially resolved material removal rates. Furthermore, the analysis of the feature importance via Shapley Additive Explanations of the models reveals that polishing time and carrier rotation are among the most critical factors influencing MRR variability.
