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Goupil is a software library designed for the Monte Carlo transport of low-energy gamma-rays, such as those emitted from radioactive isotopes. The library is distributed as a Python module. It implements a dedicated backward sampling algorithm that is highly effective for transporting photons in low density media. In the air, computation time savings of five orders of magnitude are achieved while maintaining an accuracy comparable to that of conventional Monte Carlo methods, to the nearest percent., Comment: 50 pages, 8 figures. Submitted to CPC

The rapid advancement of large language models (LLMs) has made it increasingly difficult to distinguish between text written by humans and machines. While watermarks already exist for LLMs, they often lack flexibility, and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-feature method for generating watermarks that combines multiple distinct watermark features into an ensemble watermark. Concretely, we combine acrostica and sensorimotor norms with the established red-green watermark to achieve a 98% detection rate. After a paraphrasing attack the performance remains high with 95% detection rate. The red-green feature alone as baseline achieves a detection rate of 49%. The evaluation of all feature combinations reveals that the ensemble of all three consistently has the highest detection rate across several LLMs and watermark strength settings. Due to the flexibility of combining features in the ensemble, various requirements and trade-offs can be addressed. Additionally, for all ensemble configurations the same detection function can be used without adaptations. This method is particularly of interest to facilitate accountability and prevent societal harm., Comment: 9 pages in the main body. Code is available at http://github.com/CommodoreEU/master-generation. arXiv admin note: substantial text overlap with arXiv:2405.08400

In this paper, we consider the process of transforming causal domain knowledge into a representation that aligns more closely with guidelines from causal data science. To this end, we introduce two novel tasks related to distilling causal domain knowledge into causal variables and detecting interaction entities using LLMs. We have determined that contemporary LLMs are helpful tools for conducting causal modeling tasks in collaboration with human experts, as they can provide a wider perspective. Specifically, LLMs, such as GPT-4-turbo and Llama3-70b, perform better in distilling causal domain knowledge into causal variables compared to sparse expert models, such as Mixtral-8x22b. On the contrary, sparse expert models such as Mixtral-8x22b stand out as the most effective in identifying interaction entities. Finally, we highlight the dependency between the domain where the entities are generated and the performance of the chosen LLM for causal modeling., Comment: 13 pages, 6 figutrd, 4 tabels

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices; Matches published version

The rapid advancement of large language models (LLMs) has made it increasingly difficult to distinguish between text written by humans and machines. Addressing this, we propose a novel method for generating watermarks that strategically alters token probabilities during generation. Unlike previous works, this method uniquely employs linguistic features such as stylometry. Concretely, we introduce acrostica and sensorimotor norms to LLMs. Further, these features are parameterized by a key, which is updated every sentence. To compute this key, we use semantic zero shot classification, which enhances resilience. In our evaluation, we find that for three or more sentences, our method achieves a false positive and false negative rate of 0.02. For the case of a cyclic translation attack, we observe similar results for seven or more sentences. This research is of particular of interest for proprietary LLMs to facilitate accountability and prevent societal harm., Comment: 19 pages, 4 figures, 9 tables

Throughout the past decade, research in HCI has identified numerous instances of dark patterns in digital interfaces. These efforts have led to a well-fostered typology describing harmful strategies users struggle to navigate. However, an in-depth understanding of the underlying mechanisms that deceive, coerce, or manipulate users is missing. We explore the interplay between cognitive biases and dark patterns to address this gap. To that end, we conducted four focus groups with experts (N=15) in psychology and dark pattern scholarship, inquiring how they conceptualise the relation between cognitive biases and dark patterns. Based on our results, we constructed the "Relationship Model of Cognitive Biases and Dark Patterns" which illustrates how cognitive bias and deceptive design patterns relate and identifies opportune moments for ethical reconsideration and user protection mechanisms. Our insights contribute to the current discourse by emphasising ethical design decisions and their implications in the field of HCI., Comment: This is the author-version of this work. The paper is submitted as a draft, as it is still under review, but was published at arXiv in support of a cumulative dissertation

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people's trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort's insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme's ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach., Comment: 18 pages; 4 tables; and 1 figure. This is the author's version and pre-print of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA, https://doi.org/https://doi.org/10.1145/3613904.3642542

Subject movement during the magnetic resonance examination is inevitable and causes not only image artefacts but also deteriorates the homogeneity of the main magnetic field (B0), which is a prerequisite for high quality data. Thus, characterization of changes to B0, e.g. induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-tuned the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. We therefore conclude that it is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of magnetic resonance acquisitions without the use of navigators., Comment: 9 pages, 6 figures

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page (v2: fixed name spelling of one author)

While walking meetings offer a healthy alternative to sit-down meetings, they also pose practical challenges. Taking notes is difficult while walking, which limits the potential of walking meetings. To address this, we designed the Walking Talking Stick -- a tangible device with integrated voice recording, transcription, and a physical highlighting button to facilitate note-taking during walking meetings. We investigated our system in a three-condition between-subjects user study with thirty pairs of participants ($N$=60) who conducted 15-minute outdoor walking meetings. Participants either used clip-on microphones, the prototype without the button, or the prototype with the highlighting button. We found that the tangible device increased task focus, and the physical highlighting button facilitated turn-taking and resulted in more useful notes. Our work demonstrates how interactive artifacts can incentivize users to hold meetings in motion and enhance conversation dynamics. We contribute insights for future systems which support conducting work tasks in mobile environments., Comment: In CHI 2023

We present an algorithm for simulating reverse Monte Carlo decays given an existing forward Monte Carlo decay engine. This algorithm is implemented in the Alouette library, a TAUOLA thin wrapper for simulating decays of tau-leptons. We provide a detailed description of Alouette, as well as validation results., Comment: 30 pages, 4 figures

The PUMAS library is a transport engine for muon and tau leptons in matter. It can operate with a configurable level of details, from a fast deterministic CSDA mode to a detailed Monte~Carlo simulation. A peculiarity of PUMAS is that it is revertible, i.e. it can run in forward or in backward mode. Thus, the PUMAS library is particularly well suited for muography applications. In the present document, we provide a detailed description of PUMAS, of its physics and of its implementation., Comment: 72 pages, 13 figures

There is a recommended body posture and hand position for playing every musical instrument, allowing efficient and quick movements without blockage. Due to humans' limited cognitive capabilities, they struggle to concentrate on several things simultaneously and thus sometimes lose the correct position while playing their instrument. Incorrect positions when playing an instrument can lead to injuries and movement disorders in the long run. Previous work in HCI mainly focused on developing systems to assist in learning an instrument. However, the design space for posture correction when playing a musical instrument has not yet been explored. In this position paper, we present our vision of providing subtle vibrotactile or thermal feedback to guide the focus of attention back to the correct posture when playing a musical instrument. We discuss our concept with a focus on motion recognition and feedback modalities. Finally, we outline the next steps for future research., Comment: CHI 22, Workshop "Intelligent Music Interfaces: When Interactive Assistance and Augmentation Meet Musical Instruments"

Tau neutrinos are the least studied particle in the Standard Model. This whitepaper discusses the current and expected upcoming status of tau neutrino physics with attention to the broad experimental and theoretical landscape spanning long-baseline, beam-dump, collider, and astrophysical experiments. This whitepaper was prepared as a part of the NuTau2021 Workshop., Comment: 155 pages, 63 figures, 10 tables, 69 endorsers, comments welcome, Contribution to Snowmass 2021. v2: published version