Auditory Self-Motion Simulation is Facilitated by Haptic and Vibrational Cues Suggesting the Possibility of Actual Motion

Sound fields rotating around stationary blindfolded listeners sometimes elicit auditory circular vection, the illusion that the listener is physically rotating. Experiment 1 investigated whether auditory circular vection depends on participants\u27 situational awareness of "movability", i.e., whether they sense/know that actual motion is possible or not. While previous studies often seated participants on movable chairs to suspend the disbelief of self-motion, it has never been investigated whether this does, in fact, facilitate auditory vection. To this end, 23 blindfolded participants were seated on a hammock chair with their feet either on solid ground ("movement impossible") or suspended ("movement possible") while listening to individualized binaural recordings of two sound sources rotating synchronously at 60 degrees. Although participants never physically moved, situational awareness of movability facilitated auditory vection. Moreover, adding slight vibrations like the ones resulting from actual chair rotation increased the frequency and intensity of vection. Experiment 2 extended these findings and showed that nonindividualized binaural recordings were as effective in inducing auditory circular vection as individualized recordings. These results have important implications both for our theoretical understanding of self-motion perception and for the applied field of self-motion simulations, where vibrations, non-individualized binaural sound, and the cognitive/perceptual framework of movability can typically be provided at minimal cost and effort

ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification

Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP. Here, we study technical reasons that underlie this problem. Based on this analysis, we propose practical methods and heuristics for preparing NLP datasets and models in a way that renders them amenable to known verification methods based on abstract interpretation. We implement these methods as a Python library called ANTONIO that links to the neural network verifiers ERAN and Marabou. We perform evaluation of the tool using an NLP dataset R-U-A-Robot suggested as a benchmark for verifying legally critical NLP applications. We hope that, thanks to its general applicability, this work will open novel possibilities for including NLP verification problems into neural network verification competitions, and will popularise NLP problems within this community.Comment: To appear in proceedings of 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (Affiliated with CAV 2023

Geometric phase and dimensionality reduction in locomoting living systems

The apparent ease with which animals move requires the coordination of their many degrees of freedom to manage and properly utilize environmental interactions. Identifying effective strategies for locomotion has proven challenging, often requiring detailed models that generalize poorly across modes of locomotion, body morphologies, and environments. We present the first biological application of a gauge-theory-based geometric framework for movement, originally proposed by Wilczek and Shapere nearly $40$ years ago, to describe self-deformation-driven movements through dissipative environments. Using granular resistive force theory to model environmental forces and principal components analysis to identify a low-dimensional space of animal postures and dynamics, we show that our approach captures key features of how a variety of animals, from undulatory swimmers and slitherers to sidewinding rattlesnakes, coordinate body movements and leverage environmental interactions to generate locomotion. Our results demonstrate that this geometric approach is a powerful and general framework that enables the discovery of effective control strategies, which could be further augmented by physiologically-relevant parameters and constraints to provide a deeper understanding of locomotion in a wide variety of biological systems and environments

Volumetric optoacoustic neurobehavioral tracking of epileptic seizures in freely-swimming zebrafish larvae

Fast three-dimensional imaging of freely-swimming zebrafish is essential to understand the link between neuronal activity and behavioral changes during epileptic seizures. Studying the complex spatiotemporal patterns of neuronal activity at the whole-brain or -body level typically requires physical restraint, thus hindering the observation of unperturbed behavior. Here we report on real-time volumetric optoacoustic imaging of aberrant circular swimming activity and calcium transients in freely behaving zebrafish larvae, continuously covering their motion across an entire three-dimensional region. The high spatiotemporal resolution of the technique enables capturing ictal-like epileptic seizure events and quantifying their propagation speed, independently validated with simultaneous widefield fluorescence recordings. The work sets the stage for discerning functional interconnections between zebrafish behavior and neuronal activity for studying fundamental mechanisms of epilepsy and in vivo validation of treatment strategies

Reuters Institute Digital News Report 2023. Länderbericht Schweiz

Der "Digital News Report" des Reuters Institute of Journalism / University of Oxford bietet aktuelle Daten und Erkenntnisse über den digitalen Nachrichtenkonsum, basierend auf einer Umfrage unter online Nachrichtenkonsumenten in über 45 Ländern, darunter der Schweiz. Mit seinem Report spürt das renommierte Institut der University of Oxford generelle Trends und nationale Besonderheiten auf. Das Forschungszentrum Öffentlichkeit und Gesellschaft ist seit 2016 Kooperationspartner des Reuters Institute und mitverantwortlich für die Schweizer Studienergebnisse

Pushing the mass limit for intact launch and photoionization of large neutral biopolymers

Since their first discovery by Louis Dunoyer and Otto Stern, molecular beams have conquered research and technology. However, it has remained an outstanding challenge to isolate and photoionize beams of massive neutral polypeptides. Here we show that femtosecond desorption from a matrix-free sample in high vacuum can produce biomolecular beams at least 25 times more efficiently than nanosecond techniques. While it has also been difficult to photoionize large biomolecules, we find that tailored structures with an abundant exposure of tryptophan residues at their surface can be ionized by vacuum ultraviolet light. The combination of these desorption and ionization techniques allows us to observe molecular beams of neutral polypeptides with a mass exceeding 20,000 amu. They are composed of 50 amino acids – 25 tryptophan and 25 lysine residues – and 26 fluorinated alkyl chains. The tools presented here offer a basis for the preparation, control and detection of polypeptide beams

How Can Scientific Literature Support Decision-Making in the Renovation of Historic Buildings?:An Evidence-Based Approach for Improving the Performance of Walls

Buildings of heritage significance due to their historical, architectural, or cultural value, here called historic buildings, constitute a large proportion of the building stock in many countries around the world. Improving the performance of such buildings is necessary to lower the carbon emissions of the stock, which generates around 40% of the overall emissions worldwide. In historic buildings, it is estimated that heat loss through external walls contributes significantly to the overall energy consumption, and is associated with poor thermal comfort and indoor air quality. Measures to improve the performance of walls of historic buildings require a balance between energy performance, indoor environmental quality, heritage significance, and technical compatibility. Appropriate wall measures are available, but the correct selection and implementation require an integrated process throughout assessment (planning), design, construction, and use. Despite the available knowledge, decision-makers often have limited access to robust information on tested retrofit measures, hindering the implementation of deep renovation. This paper provides an evidence-based approach on the steps required during assessment, design, and construction, and after retrofitting through a literature review. Moreover, it provides a review of possible measures for wall retrofit within the deep renovation of historic buildings, including their advantages and disadvantages and the required considerations based on context

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License