Search

To detect ultra-high-energy neutrinos, experiments such as ARA and RNO-G target the radio emission these particles induce when cascading in the ice, using deep antennas in South Pole or in Greenland. One of the main backgrounds for such signals is the radio emission generated by cosmic-ray showers, either directly in the ice, or in the air and transmitted to the ice, which can both reach the deep antennas. The first detection of cosmic rays with deep antennas would thus validate this detection principle and allow us to calibrate the detectors. FAERIE, the Framework for the simulation of Air shower Emission of Radio for in-Ice Experiments, is a numerical tool that couples both CoREAS and GEANT4 Monte-Carlo codes to simulate the radio emission from cosmic-ray showers deep in the ice. Using this code, we will investigate cosmic-ray radio signatures and the possible implications on the design of a cosmic-ray veto., Comment: 8 pages, 5 figures. Proceeding of the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA)

Boosting is an extremely successful idea, allowing one to combine multiple low accuracy classifiers into a much more accurate voting classifier. In this work, we present a new and surprisingly simple Boosting algorithm that obtains a provably optimal sample complexity. Sample optimal Boosting algorithms have only recently been developed, and our new algorithm has the fastest runtime among all such algorithms and is the simplest to describe: Partition your training data into 5 disjoint pieces of equal size, run AdaBoost on each, and combine the resulting classifiers via a majority vote. In addition to this theoretical contribution, we also perform the first empirical comparison of the proposed sample optimal Boosting algorithms. Our pilot empirical study suggests that our new algorithm might outperform previous algorithms on large data sets.

Recent works on the parallel complexity of Boosting have established strong lower bounds on the tradeoff between the number of training rounds $p$ and the total parallel work per round $t$. These works have also presented highly non-trivial parallel algorithms that shed light on different regions of this tradeoff. Despite these advancements, a significant gap persists between the theoretical lower bounds and the performance of these algorithms across much of the tradeoff space. In this work, we essentially close this gap by providing both improved lower bounds on the parallel complexity of weak-to-strong learners, and a parallel Boosting algorithm whose performance matches these bounds across the entire $p$ vs.~$t$ compromise spectrum, up to logarithmic factors. Ultimately, this work settles the true parallel complexity of Boosting algorithms that are nearly sample-optimal.

Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, new capabilities need software solutions that are able to effectively harness its power. However, developers face significant challenges when developing quantum software due to the high computational demands of simulating quantum computers on classical systems. In this paper, we investigate the potential of using an accessible and cost-efficient manner remote computational capabilities to improve the experience of quantum software developers.

Superconductivity and magnetism are antagonistic quantum matter, while their intertwining has long been considered in frustrated-lattice systems1-3. In this work, we utilize scanning tunneling microscopy and muon spin resonance to discover time-reversal symmetry-breaking superconductivity in kagome metal Cs(V,Ta)3Sb5, where the Cooper pairing exhibits magnetism and is modulated by it. In the magnetic channel, we observe spontaneous internal magnetism in a full-gap superconducting state. Under perturbations of inverse magnetic fields, we detect a time-reversal asymmetrical interference of Bogoliubov quasi-particles at a circular vector. At this vector, the pairing gap spontaneously modulates, which is distinct from pair density waves occurring at a point vector and consistent with the theoretical proposal of unusual interference effect under time-reversal symmetry-breaking. The correlation between internal magnetism, Bogoliubov quasi-particles, and pairing modulation provides a chain of experimental clues for time-reversal symmetry-breaking kagome superconductivity., Comment: To appear in Nature Materials (2024)

Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science. Their versatility, while beneficial, poses a barrier for establishing standardized best practices within the field. To bring clarity on the values of different strategies, we present an overview of the performance of modern LLM-based classification methods on a benchmark of 23 social knowledge tasks. Our results point to three best practices: select models with larger vocabulary and pre-training corpora; avoid simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific data, and consider more complex forms instruction-tuning on multiple datasets only when only training data is more abundant., Comment: 5 pages, 1 table

Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\cite{deng2023chgnet} has the potential to succeed out of the box, but can also fail significantly in predicting the ground state configuration. We demonstrate that via fine-tuning or a $\Delta$-learning approach it is possible to augment the overall performance of universal potentials for specific cluster and surface systems. We utilize this to investigate and explain experimentally observed defects in the Ag(111)-O surface reconstruction and explain the mechanics behind its formation.

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of 2-dimensional pyroxene, 3-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles., Comment: 12 pages, 10 figures

Computation of a grid of self consistent 1D model atmospheres of cool stars, sub-stellar objects and exoplanets in the effective temperature range 300K to 3000K, including cloud formation, chemical non-equilibrium effects, and stellar irradiation. The models are called MSG, because they are based on an iterative coupling between three well tested codes, the MARCS stellar atmosphere code, the StaticWeather cloud formation code and the GGchem chemical equilibrium code. It includes up-to-date molecular and atomic opacities, cloud formation and advanced chemical equilibrium calculations, and involves new numerical methods at low temperatures to allow robust convergence. The coupling between the MARCS radiative transfer and GGchem chemical equilibrium computations has made it possibly effectively to reach convergence based on electron pressure for the warmer models and gas pressure for the cooler models, enabling self-consistent modelling of stellar, sub-stellar and exoplanetary objects in a very wide range of effective temperatures. Here we describe the basic details of the models, with illustrative examples of cloudy and irradiated models as well as models based on non-equilibrium chemistry. The qualitative changes in the relative abundances of TiO, H2O, CH4, NH3, and other molecules in our models follow the observationally defined M, L, T (and Y) sequences, but reveal more complex and depth dependent abundance changes, and therefore a spectral classification depending on more parameters. The self consistent coupling to Static-Weather cloud computations, allows detailed comparison between nucleation and observed relative dimming of different spectral bands, with advanced applications for new identification methods of potential exoplanetary biology.

In this paper we consider calibration of hydraulic models for district heating systems based on operational data. We extend previous theoretical work on the topic to handle real-world complications, namely unknown valve characteristics and hysteresis. We generate two datasets in the Smart Water Infrastructure laboratory in Aalborg, Denmark, on which we evaluate the proposed procedure. In the first data set the system is controlled in such a way to excite all operational modes in terms of combinations of valve set-points. Here the best performing model predicted volume flow rates within roughly 5 and 10 \% deviation from the mean volume flow rate for the consumer with the highest and lowest mean volume flow rates respectively. This performance was met in the majority of the operational region. In the second data set, the system was controlled in order to mimic real load curves. The model trained on this data set performed similarly well when evaluated on data in the operational range represented in the training data. However, the model performance deteriorated when evaluated on data which was not represented in the training data.

Transport properties play a crucial role in defining materials as insulators, metals, or superconductors. A fundamental parameter in this regard is the Drude weight, which quantify the ballistic transport of charge carriers. In this work, we measure the Drude weights of an ultracold gas of interacting bosonic atoms confined to one dimension, characterising the induced atomic and energy currents in response to perturbations with an external potential. We induce currents through two distinct experimental protocols; by applying a constant force to the gas, and by joining two subsystems prepared in different equilibrium states. By virtue of integrability, dynamics of the system is governed by ballistically propagating, long-lived quasi-particle excitations, whereby Drude weights almost fully characterise large-scale transport. Indeed, our results align with predictions from a recently developed hydrodynamic theory, demonstrating almost fully dissipationless transport, even at finite temperatures and interactions. These findings not only provide experimental validation of the hydrodynamic predictions but also offer methodologies applicable to various condensed matter systems, facilitating further studies on the transport properties of strongly correlated quantum matter., Comment: 10 pages, 6 figures. Comments are welcome

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

In the dynamic landscape of contemporary business, the wave in data and technological advancements has directed companies toward embracing data-driven decision-making processes. Despite the vast potential that data holds for strategic insights and operational efficiencies, substantial challenges arise in the form of data issues. Recognizing these obstacles, the imperative for effective data governance (DG) becomes increasingly apparent. This research endeavors to bridge the gap in DG research within the Operations and Supply Chain Management (OSCM) domain through a comprehensive literature review. Initially, we redefine DG through a synthesis of existing definitions, complemented by insights gained from DG practices. Subsequently, we delineate the constituent elements of DG. Building upon this foundation, we develop an analytical framework to scrutinize the collected literature from the perspectives of both OSCM and DG. Beyond a retrospective analysis, this study provides insights for future research directions. Moreover, this study also makes a valuable contribution to the industry, as the insights gained from the literature are directly applicable to real-world scenarios.

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a "double robustness" property analogous to that of popular partially linear regression estimators. In contrast, the conventional VAR confidence interval with short-to-moderate lag length can severely undercover, even for misspecification that is small, economically plausible, and difficult to detect statistically. There is no free lunch: the VAR confidence interval has robust coverage only if the lag length is so large that the interval is as wide as the LP interval.

One of the most promising technologies inside the Extended Reality (XR) spectrum is Augmented Reality. This technology is already in people's pockets regarding Mobile Augmented Reality with their smartphones. The scientific community still needs answers about how humans could and should interact in environments where perceived stimuli are different from fully physical or digital circumstances. Moreover, it is still being determined if people accept these new technologies in different social environments and interaction settings or if some obstacles could exist. This paper explores the impact of the Social Environment and the Focus of social interaction on users while playing a location-based augmented reality game, measuring it with user experience and social acceptance indicators. An empirical study in a within-subject fashion was performed in different social environments and under different settings of social interaction focus with N = 28 participants compiling self-reported questionnaires after playing a Scavenger Hunt in Augmented Reality. The measures from two different Social Environments (Crowded vs. Uncrowded) resulted in statistically relevant mean differences with indicators from the Social Acceptability dimension. Moreover, the analyses show statistically relevant differences between the variances from different degrees of Social Interaction Focus with Overall Social Presence, Perceived Psychological Engagement, Perceived Attentional Engagement, and Perceived Emotional Contagion. The results suggest that a location-based AR game played in different social environments and settings can influence the user experience's social dimension. Therefore, they should be carefully considered while designing immersive technological experiences in public spaces involving social interactions between players.

BERT-based models for Electronic Health Records (EHR) have surged in popularity following the release of BEHRT and Med-BERT. Subsequent models have largely built on these foundations despite the fundamental design choices of these pioneering models remaining underexplored. To address this issue, we introduce CORE-BEHRT, a Carefully Optimized and Rigorously Evaluated BEHRT. Through incremental optimization, we isolate the sources of improvement for key design choices, giving us insights into the effect of data representation and individual technical components on performance. Evaluating this across a set of generic tasks (death, pain treatment, and general infection), we showed that improving data representation can increase the average downstream performance from 0.785 to 0.797 AUROC, primarily when including medication and timestamps. Improving the architecture and training protocol on top of this increased average downstream performance to 0.801 AUROC. We then demonstrated the consistency of our optimization through a rigorous evaluation across 25 diverse clinical prediction tasks. We observed significant performance increases in 17 out of 25 tasks and improvements in 24 tasks, highlighting the generalizability of our findings. Our findings provide a strong foundation for future work and aim to increase the trustworthiness of BERT-based EHR models., Comment: 11 pages, 5 figures

A general setting for nested subdivisions of a bounded real set into intervals defining the digits $X_1,X_2,...$ of a random variable $X$ with a probability density function $f$ is considered. Under the weak condition that $f$ is almost everywhere lower semi-continuous, a coupling between $X$ and a non-negative integer-valued random variable $N$ is established so that $X_1,...,X_N$ have an interpretation as the ``sufficient digits'', since the distribution of $R=(X_{N+1},X_{N+2},...)$ conditioned on $S=(X_1,...,X_N)$ does not depend on $f$. Adding a condition about a Markovian structure of the lengths of the intervals in the nested subdivisions, $R\,|\,S$ becomes a Markov chain of a certain order $s\ge0$. If $s=0$ then $X_{N+1},X_{N+2},...$ are IID with a known distribution. When $s>0$ and the Markov chain is uniformly geometric ergodic, a coupling is established between $(X,N)$ and a random time $M$ so that the chain after time $\max\{N,s\}+M-s$ is stationary and $M$ follows a simple known distribution. The results are related to several examples of number representations generated by a dynamical system, including base-$q$ expansions, generalized L\"uroth series, $\beta$-expansions, and continued fraction representations. The importance of the results and some suggestions and open problems for future research are discussed.

Let $X=\sum_{k=1}^\infty X_k \beta^{-k}$ be the base-$\beta$ expansion of a continuous random variable $X$ on the unit interval where $\beta$ is the positive solution to $\beta^n = 1 + \beta + \cdots + \beta^{n-1}$ for an integer $n\ge 2$ (i.e., $\beta$ is a generalization of the golden mean for which $n=2$). We study the asymptotic distribution and convergence rate of the scaled remainder $\sum_{k=1}^\infty X_{m+k} \beta^{-k}$ when $m$ tends to infinity., Comment: 13 pages

Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo endoscopic subscore (MES) index is the standard for measuring UC severity during endoscopic evaluation. However, the MES is subject to high inter-observer variability leading to misdiagnosis and suboptimal treatment. We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability. The system runs real-time in the clinic and augments doctors' decision-making during the endoscopy. This project report outlines the process of designing, creating and evaluating our system. We describe our initial evaluation, which is a combination of a standard non-clinical model test and a first clinical test of the system on a real patient.

The aim of this study was to investigate the difference in output, when training a model in three different scenarios: a large clinical delineated data set (with 700/78 patients for training/testing, from the Danish Breast Cancer Group (DBCG) RT Nation Study), a clinical but curated dataset (with 328/36 patients for training/testing, from the DBCG RT Nation Study) and a smaller, but dedicated data set created by delineation experts (with 123/14 patients for training/testing, consensus delineations created by delineation experts). The model performance was estimated based on the performance metrics dice similarity coefficient (DSC), Hausdorff 95th percentile (HD95) and mean surface distance (MSD). Models were tested in test sets from their own cohort, and afterwards also compared in the dedicated data test set. The difference between model output was finally estimated by measuring the mean width and cranial caudal length of the model output for the models. When testing the model output between the clinical models and the dedicated models in their own test set, the two clinical models had a poorer performance, than the dedicated models, but not all metrics showed statistically significance. When testing the models in the dedicated data, the dedicated model showed a slightly better performance, along with fewer segmentation outliers. As a way of taking advantage of the strength from both types of data set, it could be an option to use a large clinical data set as a baseline model, and then finetune with smaller sized cohorts with dedicated delineations., Comment: 4 pages, 2 figures, 2 tables, submitted to the ICCR 2024

Developing an optimal PAC learning algorithm in the realizable setting, where empirical risk minimization (ERM) is suboptimal, was a major open problem in learning theory for decades. The problem was finally resolved by Hanneke a few years ago. Unfortunately, Hanneke's algorithm is quite complex as it returns the majority vote of many ERM classifiers that are trained on carefully selected subsets of the data. It is thus a natural goal to determine the simplest algorithm that is optimal. In this work we study the arguably simplest algorithm that could be optimal: returning the majority vote of three ERM classifiers. We show that this algorithm achieves the optimal in-expectation bound on its error which is provably unattainable by a single ERM classifier. Furthermore, we prove a near-optimal high-probability bound on this algorithm's error. We conjecture that a better analysis will prove that this algorithm is in fact optimal in the high-probability regime., Comment: 22 pages

Our aim in this paper is to refine the orbital and physical parameters of the HATS-2 planetary system and study transit timing variations and atmospheric composition thanks to transit observations that span more than ten years and that were collected using different instruments and pass-band filters. We also investigate the orbital alignment of the system by studying the anomalies in the transit light curves induced by starspots on the photosphere of the parent star. We analysed new transit events from both ground-based telescopes and NASA's TESS mission. Anomalies were detected in most of the light curves and modelled as starspots occulted by the planet during transit events. We fitted the clean and symmetric light curves with the JKTEBOP code and those affected by anomalies with the PRISM+GEMC codes to simultaneously model the photometric parameters of the transits and the position, size, and contrast of each starspot. We found consistency between the values we found for the physical and orbital parameters and those from the discovery paper and ATLAS9 stellar atmospherical models. We identified different sets of consecutive starspot-crossing events that temporally occurred in less than five days. Under the hypothesis that we are dealing with the same starspots, occulted twice by the planet during two consecutive transits, we estimated the rotational period of the parent star and, in turn the projected and the true orbital obliquity of the planet. We find that the system is well aligned. We identified the possible presence of transit timing variations in the system, which can be caused by tidal orbital decay, and we derived a low-resolution transmission spectrum., Comment: 23 pages, 21 figures, Accepted for publication in Astronomy & Astrophysics

Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a type of structure generation, which locally optimizes structures in complementary energy (CE) landscapes. These landscapes are formulated temporarily during the searches as machine learned potentials (MLPs) using local atomistic environments sampled from collected data. The CE landscapes are deliberately incomplete MLPs that rather than mimicking every aspect of the true PES are sought to become much smoother, having only few local minima. This means that local optimization in the CE landscapes may facilitate identification of new funnels in the true PES. We discuss how to construct the CE landscapes and we test their influence on global optimization of a reduced rutile SnO2(110)-(4x1) surface, and an olivine (Mg2SiO4)4 cluster for which we report a new global minimum energy structure., Comment: 13 pages, 9 figures

The use of deep learning models in computational biology has increased massively in recent years, and is expected to do so further with the current advances in fields like Natural Language Processing. These models, although able to draw complex relations between input and target, are also largely inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to randomly split the available data in development (train/validation) and test sets. This procedure, although standard, has lately been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of the state-of-the-art model DeepLoc, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available for downloading and installing at https://github.com/genomicepidemiology/SpanSeq.

This paper introduces a new tool, OccCANINE, to automatically transform occupational descriptions into the HISCO classification system. The manual work involved in processing and classifying occupational descriptions is error-prone, tedious, and time-consuming. We finetune a preexisting language model (CANINE) to do this automatically, thereby performing in seconds and minutes what previously took days and weeks. The model is trained on 14 million pairs of occupational descriptions and HISCO codes in 13 different languages contributed by 22 different sources. Our approach is shown to have accuracy, recall, and precision above 90 percent. Our tool breaks the metaphorical HISCO barrier and makes this data readily available for analysis of occupational structures with broad applicability in economics, economic history, and various related disciplines., Comment: All code and guides on how to use OccCANINE is available on GitHub https://github.com/christianvedels/OccCANINE

We propose to estimate the weight matrix used for forecast reconciliation as parameters in a general linear model in order to quantify its uncertainty. This implies that forecast reconciliation can be formulated as an orthogonal projection from the space of base-forecast errors into a coherent linear subspace. We use variance decomposition together with the Wishart distribution to derive the central estimator for the forecast-error covariance matrix. In addition, we prove that distance-reducing properties apply to the reconciled forecasts at all levels of the hierarchy as well as to the forecast-error covariance. A covariance matrix for the reconciliation weight matrix is derived, which leads to improved estimates of the forecast-error covariance matrix. We show how shrinkage can be introduced in the formulated model by imposing specific priors on the weight matrix and the forecast-error covariance matrix. The method is illustrated in a simulation study that shows consistent improvements in the log-score. Finally, standard errors for the weight matrix and the variance-separation formula are illustrated using a case study of forecasting electricity load in Sweden., Comment: 51 pages

Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle's trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planner algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Occlusion., Comment: 8 Pages. Submitted to IEEE IV Conference, Korea

Our research introduces a modular motion planning framework for autonomous vehicles using a sampling-based trajectory planning algorithm. This approach effectively tackles the challenges of solution space construction and optimization in path planning. The algorithm is applicable to both real vehicles and simulations, offering a robust solution for complex autonomous navigation. Our method employs a multi-objective optimization strategy for efficient navigation in static and highly dynamic environments, focusing on optimizing trajectory comfort, safety, and path precision. The algorithm is used to analyze the algorithm performance and success rate in 1750 virtual complex urban and highway scenarios. Our results demonstrate fast calculation times (8ms for 800 trajectories), a high success rate in complex scenarios (88%), and easy adaptability with different modules presented. The most noticeable difference exhibited was the fast trajectory sampling, feasibility check, and cost evaluation step across various trajectory counts. We demonstrate the integration and execution of the framework on real vehicles by evaluating deviations from the controller using a test track. This evaluation highlights the algorithm's robustness and reliability, ensuring it meets the stringent requirements of real-world autonomous driving scenarios. The code and the additional modules used in this research are publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Motion-Planner., Comment: Submitted to IEEE ACCESS

Let $F:(\mathbb{C}^2,0)\to (\mathbb{C}^n,0)$ be the germ of a finite map and $(X,0)$ be its image. We will in this article using the topology of the link show that $(X,0)$ has to be a quotient singularity if it is normal and describe the possible topological types. Including a discussion of the groups and examples of how to construct a map to a given topology. We will also discuss the case when the image is not normal., Comment: 17 Pages

It is known from Earth that ionizing high-energy radiation can lead to ion-induced nucleation of cloud condensation nuclei in the atmosphere. Since the amount of high-energy radiation can vary greatly based on the radiative environment of a host star, understanding the effect of high-energy radiation on cloud particles is critical to understand exoplanet atmospheres. This study aims to explore how high-energy radiation affects the aggregation and charging of mineral cloud particles. We present experiments conducted in an atmosphere chamber on mineral SiO$_2$ particles with diameters of 50 nm. The particles were exposed to gamma radiation in either low-humidity (RH $\approx$ 20%) or high-humidity (RH $>$ 50%) environments. The aggregation and charging state of the particles were studied with a Scanning Mobility Particle Sizer. We find that the single SiO$_2$ particles (N1) cluster to form larger aggregates (N2 - N4), and that this aggregation is inhibited by gamma radiation. We find that gamma radiation shifts the charging of the particles to become more negative, by increasing the charging state of negatively charged particles. Through an independent T-test we find that this increase is statistically significant within a 5% significance level for all aggregates in the high-humidity environment, and for all except the N1 particles in the low-humidity environment. For the positively charged particles the changes in charging state are not within the 5% significance level. We suggest that the overall effect of gamma radiation could favor the formation of a high number of small particles over a lower number of larger particles., Comment: Accepted to ApJ, 19 pages, 9 figures

(Abridged). We explore the fraction of radio loud quasars in the eHAQ+GAIA23 sample, which contains quasars from the High A(V) Quasar (HAQ) Survey, the Extended High A(V) Quasar (eHAQ) Survey, and the Gaia quasar survey. All quasars in this sample have been found using a near-infrared color selection of target candidates that have otherwise been missed by the Sloan Digital Sky Survey (SDSS). We implemented a redshift-dependent color cut in g-i to select red quasars in the sample and divided them into redshift bins, while using a nearest-neighbors algorithm to control for luminosity and redshift differences between our red quasar sample and a selected blue sample from the SDSS. Within each bin, we cross-matched the quasars to the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey and determined the radio-detection fraction. We find similar radio-detection fractions for red and blue quasars within 1 sigma, independent of redshift. This disagrees with what has been found in the literature for red quasars in SDSS. It should be noted that the fraction of broad absorption line (BAL) quasars in red SDSS quasars is about five times lower than in our sample. BAL quasars have been observed to be more frequently radio quiet than other quasars, therefore the difference in BAL fractions could explain the difference in radio-detection fraction. The observed higher proportion of BAL quasars in our dataset relative to the SDSS sample, along with the higher rate of radio detections, indicates an association of the redness of quasars and the inherent BAL fraction within the overall quasar population. This finding highlights the need to explore the underlying factors contributing to both the redness and the frequency of BAL quasars, as they appear to be interconnected phenomena., Comment: 10 pages, 4 figures. Accepted for publication in A&A

The increasing capability of Large Language Models to act as human-like social agents raises two important questions in the area of opinion dynamics. First, whether these agents can generate effective arguments that could be injected into the online discourse to steer the public opinion. Second, whether artificial agents can interact with each other to reproduce dynamics of persuasion typical of human social systems, opening up opportunities for studying synthetic social systems as faithful proxies for opinion dynamics in human populations. To address these questions, we designed a synthetic persuasion dialogue scenario on the topic of climate change, where a 'convincer' agent generates a persuasive argument for a 'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. Different types of arguments were generated to incorporate different linguistic dimensions underpinning psycho-linguistic theories of opinion change. We then asked human judges to evaluate the persuasiveness of machine-generated arguments. Arguments that included factual knowledge, markers of trust, expressions of support, and conveyed status were deemed most effective according to both humans and agents, with humans reporting a marked preference for knowledge-based arguments. Our experimental framework lays the groundwork for future in-silico studies of opinion dynamics, and our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media., Comment: 9 pages, 6 figures, 3 tables, 1 page in appendix

In the hydrodynamics of integrable models, diffusion is a subleading correction to ballistic propagation. Here we quantify the diffusive contribution for one-dimensional Bose gases and find it most influential in the crossover between the main thermodynamic regimes of the gas. Analysing the experimentally measured dynamics of a single density mode, we find diffusion to be relevant only for high wavelength excitations. Instead, the observed relaxation is solely caused by a ballistically driven dephasing process, whose time scale is related to the phonon lifetime of the system and is thus useful to evaluate the applicability of the phonon bases typically used in quantum field simulators., Comment: 16 pages, 7 figures. Submission to SciPost Physics. Comments are welcome

We consider a class of toy models describing a fermion field coupled with a boson field. The model can be viewed as a Yukawa model but with scalar fermions. As in our first paper, the interaction kernels are assumed bounded in the fermionic momentum variable and decaying like $|q|^{-p}$ for large boson momenta $q$. With no restrictions on the coupling strength, we prove norm resolvent convergence to an ultraviolet renormalized Hamiltonian, when the ultraviolet cutoff is removed. We do this by subtracting a sufficiently large, but finite, number of recursively defined self-energy counter-terms, which may be interpreted as arising from a perturbation expansion of the ground state energy. The renormalization procedure requires a spatial cutoff and works in three dimensions provided $p>\frac12$, which is as close as one may expect to the physically natural exponent $p = \frac12$.

Let $X=\sum_{k=1}^\infty X_k \beta^{-k}$ be the base-$\beta$ expansion of a continuous random variable $X$ on the unit interval where $\beta$ is the golden ratio. We study the asymptotic distribution and convergence rate of the scaled remainder $\sum_{k=n+1}^\infty X_k \beta^{n-k}$ when $n$ tends to infinity., Comment: 7 pages

In this study, the performance of the doubles-corrected higher random-phase approximation (HRPA(D)) has been investigated in calculations of NMR spin-spin coupling constants (SSCCs) for 58 molecules with the experimental values used as the reference values. HRPA(D) is an approximation to the second-order polarization propagator approximation (SOPPA), and is therefore computationally less expensive than SOPPA. HRPA(D) performs comparable and sometimes even better than SOPPA, and therefore when calculating SSCCs it should be considered as an alternative to SOPPA. Furthermore, it was investigated whether a CCSD(T) or MP2 geometry optimization was optimal for a SOPPA and a HRPA(D) SSCCs calculation for 8 smaller molecules. CCSD(T) is the optimal geometry optimization for the SOPPA calculation, and MP2 was optimal for the HRPA(D) SSCC calculations.

In October 2011, Denmark introduced the world's first and, to date, only tax targeting saturated fat. However, this tax was subsequently abolished in January 2013. Leveraging exogenous variation from untaxed Northern-German consumers, we employ a difference-in-differences approach to estimate the causal effects of both the implementation and repeal of the tax on consumption and expenditure behavior across eight product categories targeted by the tax. Our findings reveal significant heterogeneity in the tax's impact across these products. During the taxed period, there was a notable decline in consumption of bacon, liver sausage, salami, and cheese, particularly among low-income households. In contrast, expenditure on butter, cream, margarine, and sour cream increased as prices rose. Interestingly, we do not observe any difference in expenditure increases between high and low-income households, suggesting that the latter were disproportionately affected by the tax. After the repeal of the tax, we do not observe any significant decline in consumption. On the contrary, there was an overall increase in consumption for certain products, prompting concerns about unintended consequences resulting from the brief implementation of the tax.

One-dimensional Mott insulators can be described using the sine-Gordon model, an integrable quantum field theory that provides the low-energy effective description of several one-dimensional gapped condensed matter systems, including recent realizations with trapped ultra-cold atoms. Employing the theory of Generalized Hydrodynamics, we demonstrate that this model exhibits separation of the transport of topological charge vs. energy. Analysis of the quasiparticle dynamics reveals that the mechanism behind the separation is the reflective scattering between topologically charged kinks/antikinks. The effect of these scattering events is most pronounced at strong coupling and low temperatures, where the distribution of quasiparticles is narrow compared to the reflective scattering amplitude. This effect results in a distinctively shaped "arrowhead" light cone for the topological charge., Comment: 19 pages, 16 figures

Based on previous work, we assess the use of NIR-HSI images for calibrating models on two datasets, focusing on protein content regression and grain variety classification. Limited reference data for protein content is expanded by subsampling and associating it with the bulk sample. However, this method introduces significant biases due to skewed leptokurtic prediction distributions, affecting both PLS-R and deep CNN models. We propose adjustments to mitigate these biases, improving mean protein reference predictions. Additionally, we investigate the impact of grain-to-background ratios on both tasks. Higher ratios yield more accurate predictions, but including lower-ratio images in calibration enhances model robustness for such scenarios., Comment: 19 pages, 17 figures

Conventional hydrodynamics describes systems with few long-lived excitations. In one dimension, however, many experimentally relevant systems feature a large number of long-lived excitations even at high temperature, because they are proximate to integrable limits. Such models cannot be treated using conventional hydrodynamics. The framework of generalized hydrodynamics (GHD) was recently developed to treat the dynamics of one-dimensional models: it combines ideas from integrability, hydrodynamics, and kinetic theory to come up with a quantitative theory of transport. GHD has successfully settled several longstanding questions about one-dimensional transport; it has also been leveraged to study dynamical questions beyond the transport of conserved quantities, and to systems that are not integrable. In this article we introduce the main ideas and predictions of GHD, survey some of the most recent theoretical extensions and experimental tests of the GHD framework, and discuss some open questions in transport that the GHD perspective might elucidate., Comment: 16 pages, 5 figures, perspective article, feedback is welcome