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In today's online environments users experience harm and abuse on a daily basis. Therefore, content moderation is crucial to ensure their safety and well-being. However, the effectiveness of many moderation interventions is still uncertain. We evaluate the effectiveness of The Great Ban, one of the largest deplatforming interventions carried out by Reddit that affected almost 2,000 communities. We analyze 53M comments shared by nearly 34K users, providing in-depth results on both the intended and unintended consequences of this ban. We found that 15.6% of the moderated users abandoned the platform while the remaining ones decreased their overall toxicity by 4.1%. Nonetheless, a subset of those users increased their toxicity by 70% after the intervention. In any case, increases in toxicity did not lead to marked increases in activity or engagement, meaning that the most toxic users had overall a limited impact. Our findings bring to light new insights on the effectiveness of deplatforming. Furthermore, they also contribute to informing future content moderation strategies., Comment: arXiv admin note: text overlap with arXiv:2401.11254 This work is an extension of this conference paper: Cima, L., Trujillo, A., Avvenuti, M., & Cresci, S. (2024, May). The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit. In Companion Publication of the 16th ACM Web Science Conference (pp. 85-93)

The development of ultra-low emittance storage rings, such as the e+/e- Future Circular Collider (FCC-ee) with a circumference of about 90 km, aims to achieve unprecedented luminosity and beam size. One significant challenge is correcting the optics, which becomes increasingly difficult as we target lower emittances. In this paper, we investigate optics correction methods to address these challenges. We examined the impact of arc region magnet alignment errors in the baseline optics for the FCC-ee lattice at Z energy. To establish realistic alignment tolerances, we developed a sequence of correction steps using the Python Accelerator Toolbox (PyAT) to correct the lattice optics, achieve the nominal emittance, Dynamic Aperture (DA), and in the end, the design luminosity. The correction scheme has been recently optimized and better machine performance demonstrated. A comparison was conducted between two optics correction approaches: Linear Optics from Closed Orbits (LOCO) with phase advance + $\eta_x$ and coupling Resonance Driving Terms (RDTs) + $\eta_y$. The latter method demonstrated better performance in achieving the target emittance and enhancing the DA., Comment: Appears in the proceedings of the 14th International Computational Accelerator Physics Conference (ICAP'24), 2-5 October 2024, Germany

Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multiobjective optimization algorithm, the NOAH algorithm by Ulrich and Thiele, the Solow Polasky Diversity (also related to Magnitude) served as a metric, other diversity indicators might be considered, such as the parameter-free Max-Min Diversity, and the Riesz s-Energy, which features uniformly distributed solution sets. In this paper, focusing on multiobjective diversity optimization, we discuss different diversity indicators from the perspective of indicator-based evolutionary algorithms (IBEA) with multiple objectives. We examine theoretical, computational, and practical properties of these indicators, such as monotonicity in species, twinning, monotonicity in distance, strict monotonicity in distance, uniformity of maximizing point sets, computational effort for a set of size~n, single-point contributions, subset selection, and submodularity. We present new theorems -- including a proof of the NP-hardness of the Riesz s-Energy Subset Selection Problem -- and consolidate existing results from the literature. In the second part, we apply these indicators in the NOAH algorithm and analyze search dynamics through an example. We examine how optimizing with one indicator affects the performance of others and propose NOAH adaptations specific to the Max-Min indicator., Comment: 14 pages

In light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer is "no" if one performs calculations within a spin-only low-energy effective spin model with non-interacting magnons. However, by performing determinant quantum Monte Carlo simulations, we show the single-band $t$-$t'$-$U$ Hubbard model coupled to an orbital magnetic field does support a finite thermal Hall effect when $t' \neq 0$ and $B \neq 0$ in the Mott insulating phase. We argue that the (carrier agnostic) necessary conditions for observing a finite thermal Hall effect are time-reversal and particle-hole symmetry breaking. By considering magnon-magnon scattering using a semi-classical Boltzmann analysis, we illustrate a physical mechanism by which finite transverse thermal conductivity may arise, consistent with our symmetry argument and numerical results. Our results contradict the conventional wisdom that square and triangular lattices with SU(2) symmetry do not support a finite thermal Hall effect and call for a critical re-examination of thermal Hall effect data in insulating magnets, as the magnon contribution should not be excluded a priori., Comment: 6+23 pages, 3+11 figures

Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) which computes feature contributions based on a game theoretical interpretation and PD plots (i.e., 1-dim PD functions) that capture average marginal main effects. Recent work has connected these approaches using a functional decomposition and argues that SHAP values can be misleading since they merge main and interaction effects into a single local effect. A major advantage of SHAP compared to other PD-based interpretations, however, has been the availability of fast estimation techniques, such as \texttt{TreeSHAP}. In this paper, we propose a new tree-based estimator, \texttt{FastPD}, which efficiently estimates arbitrary PD functions. We show that \texttt{FastPD} consistently estimates the desired population quantity -- in contrast to path-dependent \texttt{TreeSHAP} which is inconsistent when features are correlated. For moderately deep trees, \texttt{FastPD} improves the complexity of existing methods from quadratic to linear in the number of observations. By estimating PD functions for arbitrary feature subsets, \texttt{FastPD} can be used to extract PD-based interpretations such as SHAP, PD plots and higher order interaction effects.

Gravitational wave dark sirens are a powerful tool for cosmology and inference of compact object population hyperparameters. They allow for a measurement of the luminosity distance to the source, but not their redshift. Galaxy catalogues in the source localization volume can be used to infer the redshift of the source in a statistical manner. Catalogues are, however, limited by their incompleteness, which can be significant at redshifts corresponding to current GW events. In this work, we detail how to implement in practice variance completion, a novel galaxy completion method which uses knowledge of the large scale structure to optimize the potential of dark sirens analyses. We compress the prediction for the missing number of galaxies into a ratio between the predictions of variance completion and the standard homogeneous completion method. This ratio format can be easily incorporated into existing line of sight computations used in dark sirens software; we demonstrate this procedure using the GLADE+ galaxy catalogue and the gwcosmo software package. We discuss the robustness of the method, and apply it to well-localized event GW190814 as a proof of concept. Finally, we apply the method to data from the third observing run of LIGO-Virgo-KAGRA, finding that it yields results that are consistent with homogeneous completion. We also discuss the prospects for an improvement if the GW localization volume shrinks., Comment: 27 pages, 12 figures

The dark sirens method combines gravitational waves and catalogs of galaxies to constrain the cosmological expansion history, merger rates and mass distributions of compact objects, and the laws of gravity. However, the incompleteness of galaxy catalogs means faint potential host galaxies are unobserved, and must be modeled to avoid inducing a bias. The majority of dark sirens analyses to date assume that the missing galaxies are distributed uniformly across the sky, which is clearly unphysical. We introduce a new Bayesian approach to the reconstruction of galaxy catalogs, which makes full use of our knowledge of large-scale structure. Our method quantifies the uncertainties on the estimated true galaxy number count in each voxel, and is marginalized over cosmological parameters and bias parameters. Crucially, our method further assesses the (absolute) magnitude distribution of galaxies, which is not known from the galaxy catalog itself. We present the details of our method and validate our approach on a galaxy catalog associated to the Millennium Simulation. The tools developed here generate physically-informed and robust host galaxy reconstructions, enabling more informative dark sirens analyses. Stage IV galaxy surveys will display greater redshift overlap with GW observations, whilst remaining incomplete -- emphasizing the importance of our work., Comment: 43 pages, 15 figures

Hevelius, a web-based computer mouse test, measures arm movement and has been shown to accurately evaluate severity for patients with Parkinson's disease and ataxias. A Hevelius session produces 32 numeric features, which may be hard to interpret, especially in time-constrained clinical settings. This work aims to support clinicians (and other stakeholders) in interpreting and connecting Hevelius features to clinical concepts. Through an iterative design process, we developed a visualization tool (Hevelius Report) that (1) abstracts six clinically relevant concepts from 32 features, (2) visualizes patient test results, and compares them to results from healthy controls and other patients, and (3) is an interactive app to meet the specific needs in different usage scenarios. Then, we conducted a preliminary user study through an online interview with three clinicians who were not involved in the project. They expressed interest in using Hevelius Report, especially for identifying subtle changes in their patients' mobility that are hard to capture with existing clinical tests. Future work will integrate the visualization tool into the current clinical workflow of a neurology team and conduct systematic evaluations of the tool's usefulness, usability, and effectiveness. Hevelius Report represents a promising solution for analyzing fine-motor test results and monitoring patients' conditions and progressions., Comment: Accepted to the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '24)

Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose estimators determine poses without prior knowledge. Recently, vision language models (VLMs) have shown considerable advances in robotics applications by establishing an understanding between language input and image input. In our work, we take advantage of VLMs zero-shot capabilities and translate this ability to 6D object pose estimation. We propose a novel framework for promptable zero-shot 6D object pose estimation using language embeddings. The idea is to derive a coarse location of an object based on the relevancy map of a language-embedded NeRF reconstruction and to compute the pose estimate with a point cloud registration method. Additionally, we provide an analysis of LERF's suitability for open-set object pose estimation. We examine hyperparameters, such as activation thresholds for relevancy maps and investigate the zero-shot capabilities on an instance- and category-level. Furthermore, we plan to conduct robotic grasping experiments in a real-world setting.

Humans have clear cross-modal preferences when matching certain novel words to visual shapes. Evidence suggests that these preferences play a prominent role in our linguistic processing, language learning, and the origins of signal-meaning mappings. With the rise of multimodal models in AI, such as vision- and-language (VLM) models, it becomes increasingly important to uncover the kinds of visio-linguistic associations these models encode and whether they align with human representations. Informed by experiments with humans, we probe and compare four VLMs for a well-known human cross-modal preference, the bouba-kiki effect. We do not find conclusive evidence for this effect but suggest that results may depend on features of the models, such as architecture design, model size, and training details. Our findings inform discussions on the origins of the bouba-kiki effect in human cognition and future developments of VLMs that align well with human cross-modal associations., Comment: Appeared at the 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2024)

Natural language has the universal properties of being compositional and grounded in reality. The emergence of linguistic properties is often investigated through simulations of emergent communication in referential games. However, these experiments have yielded mixed results compared to similar experiments addressing linguistic properties of human language. Here we address representational alignment as a potential contributing factor to these results. Specifically, we assess the representational alignment between agent image representations and between agent representations and input images. Doing so, we confirm that the emergent language does not appear to encode human-like conceptual visual features, since agent image representations drift away from inputs whilst inter-agent alignment increases. We moreover identify a strong relationship between inter-agent alignment and topographic similarity, a common metric for compositionality, and address its consequences. To address these issues, we introduce an alignment penalty that prevents representational drift but interestingly does not improve performance on a compositional discrimination task. Together, our findings emphasise the key role representational alignment plays in simulations of language emergence., Comment: Appeared at the 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2024)

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution., Comment: Accepted to CoNLL2024

The $\texttt{Hi-COLA}$ code is an efficient dark matter simulation suite that flexibly handles the Horndeski family of modified gravity models. In this work we extend the scope of $\texttt{Hi-COLA}$ to accommodate Horndeski theories with K-mouflage screening, allowing for the computation of matter power spectra in the non-linear regime in these models. We explore the boost of the dark matter power spectrum relative to GR-$\Lambda$CDM in K-mouflage gravity, and also discuss how large-scale structure computations change between the Einstein and Jordan frames. A dissection of the relative contributions of the modified background, linear growth, fifth force, and the conformal factor (a new inclusion to $\texttt{Hi-COLA}$) to the boost factor is presented. The ability of $\texttt{Hi-COLA}$ to run with general Horndeski models and multiple screening mechanisms makes it an ideal tool for testing gravity with upcoming galaxy survey data., Comment: 31 pages, 10 figures, 1 table. Comments welcome. The Hi-COLA code can be found at https://github.com/Hi-COLACode/Hi-COLA

This study investigates whether division on political topics is mapped with the distinctive patterns of language use. We collect a total 145,832 Reddit comments on the abortion debate and explore the languages of subreddit communities r/prolife and r/prochoice. With consideration of the Moral Foundations Theory, we examine lexical patterns in three ways. First, we compute proportional frequencies of lexical items from the Moral Foundations Dictionary in order to make inferences about each group's moral considerations when forming arguments for and against abortion. We then create n-gram models to reveal frequent collocations from each stance group and better understand how commonly used words are patterned in their linguistic context and in relation to morality values. Finally, we use Latent Dirichlet Allocation to identify underlying topical structures in the corpus data. Results show that the use of morality words is mapped with the stances on abortion.

Testing gravity and the concordance model of cosmology, $\Lambda$CDM, at large scales is a key goal of this decade's largest galaxy surveys. Here we present a comparative study of dark matter power spectrum predictions from different numerical codes in the context of three popular theories of gravity that induce scale-independent modifications to the linear growth of structure: nDGP, Cubic Galileon and K-mouflage. In particular, we compare the predictions from full $N$-body simulations, two $N$-body codes with approximate time integration schemes, a parametrised modified $N$-body implementation and the analytic halo model reaction approach. We find the modification to the $\Lambda$CDM spectrum is in $2\%$ agreement for $z\leq1$ and $k\leq 1~h/{\rm Mpc}$ over all gravitational models and codes, in accordance with many previous studies, indicating these modelling approaches are robust enough to be used in forthcoming survey analyses under appropriate scale cuts. We further make public the new code implementations presented, specifically the halo model reaction K-mouflage implementation and the relativistic Cubic Galileon implementation., Comment: 20 pages, 4 figures, 4 tables

We present radio observations of the galaxy cluster Abell S1136 at 888 MHz, using the Australian Square Kilometre Array Pathfinder radio telescope, as part of the Evolutionary Map of the Universe Early Science program. We compare these findings with data from the Murchison Widefield Array, XMM-Newton, the Wide-field Infrared Survey Explorer, the Digitised Sky Survey, and the Australia Telescope Compact Array. Our analysis shows the X-ray and radio emission in Abell S1136 are closely aligned and centered on the BCG, while the X-ray temperature profile shows a relaxed cluster with no evidence of a cool core. We find that the diffuse radio emission in the centre of the cluster shows more structure than seen in previous low-resolution observations of this source, which appeared formerly as an amorphous radio blob, similar in appearance to a radio halo; our observations show the diffuse emission in the Abell S1136 galaxy cluster contains three narrow filamentary structures visible at 888 MHz, between$\sim 80$ and 140 kpc in length; however the properties of the diffuse emission do not fully match that of a radio (mini-)halo or (fossil) tailed radio source., Comment: To appear in PASA

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observations. Existing DRL intraday trading strategies mainly use price-based features to construct the state space. They neglect the contextual information related to the position of the strategy, which is an important aspect given the sequential nature of intraday trading. In this study, we propose a novel DRL model for intraday trading that introduces positional features encapsulating the contextual information into its sparse state space. The model is evaluated over an extended period of almost a decade and across various assets including commodities and foreign exchange securities, taking transaction costs into account. The results show a notable performance in terms of profitability and risk-adjusted metrics. The feature importance results show that each feature incorporating contextual information contributes to the overall performance of the model. Additionally, through an exploration of the agent's intraday trading activity, we unveil patterns that substantiate the effectiveness of our proposed model.

We investigate the instability of layer pseudospin paramagnetic (PSP) state to the formation of pseudospin density wave (PSDW) in two-dimensional (2D) electron bilayers, analogous to the formation of Overhauser spin density wave (SDW) in a single-layer 2D electron gas (2DEG) with spin 1/2. Our comprehensive study on phase diagrams, based on the self-consistent Hartree-Fock (HF) theory, reveals that the PSDW has a lower energy than both PSP and pseudospin ferromagnetic (PSF) states near the PSP-PSF phase transition boundary. When the two layers are populated by the same number of electrons, the PSDW momentum $Q_c \sim 2k_F$ near the PSP-PSDW boundary, where $k_F= (2\pi n)^{1/2}$ is the Fermi momentum characterized by the density in one of the two layers, and $Q_c$ decreases as the system transitions to the PSF regime. Extending the HF study to the case of unequal layer densities, the PSP phase is unstable to PSDW for small density imbalances, with momentum $Q_c \sim k_{F,t} + k_{F,b}$, where $k_{F,t}$ and $k_{F,b}$ are Fermi momenta of top and bottom layers, respectively. In PSDW regime, the ground state stability, defined by the energy difference between PSDW and the second lowest-energy state, is one order of magnitude lower than that in PSF regime, and decreases with increasing layer separation $d$. Furthermore, incorporating RPA static screening with the Hubbard-type local field correction leads to disappearance of both SDW and PSDW phases, and pushes the phase boundaries of paramagnetic to ferromagnetic transitions to larger $r_s$ values. Our study on PSDW in 2D electron bilayers is equally applicable to 2D hole bilayers. The idea of pursuing PSDW is, in general, relevant across various 2D bilayer systems, not limited to the parabolic model that we investigate in this paper, and provides a new possibility of exploring novel coherent phases.

Among the bent tail radio galaxies common in galaxy clusters are some with long, collimated tails (so-called head-tail galaxies) shaped by their interactions with the intracluster medium (ICM). Here we report the discovery of intricate filamentary structure in and beyond the ~28' (570 kpc) long, helical radio tail of the Corkscrew Galaxy (1610-60.5, ESO137-G007), which resides in the X-ray bright cluster Abell 3627 (D = 70 Mpc). Deep radio continuum data were obtained with wide-field Phased Array Feeds on the Australian Square Kilometer Array Pathfinder (ASKAP) at 944 MHz and 1.4 GHz. The Corkscrew Galaxy is located 15' north of the prominent wide-angle tail (WAT) radio galaxy 1610-60.8 (ESO137-G006) near the cluster centre. While the bright (young) part of its radio tail is highly collimated, the faint (old) part shows increasing oscillation amplitudes, break-ups, and filaments. We find a stunning set of arc-shaped radio filaments beyond and mostly orthogonal to the collimated Corkscrew tail end, forming a partial bubble. This may be the first detection of a "proto-lobe" seen in 3D MHD simulations by Nolting et al. (2019), formed by the face-on impact of the Corkscrew Galaxy with a shock front in the cluster outskirts. Interactions of the radio galaxy tail with the ICM are likely responsible for the tail collimation and shear forces within the ICM for its increasingly filamentary structure. We also report the discovery of small (~20-30 kpc) ram-pressure stripped radio tails in four Abell 3627 cluster galaxies., Comment: 11 pages, 7 figures, MNRAS, submitted

Both Schulze and ranked pairs are voting rules that satisfy many natural, desirable axioms. Many standard types of electoral control (with a chair seeking to change the outcome of an election by interfering with the election structure) have already been studied. However, for control by replacing candidates or voters and for (exact) multimode control that combines multiple standard attacks, many questions remain open. We solve a number of these open cases for Schulze and ranked pairs. In addition, we fix a flaw in the reduction of Menton and Singh [IJCAI 2013] showing that Schulze is resistant to constructive control by deleting candidates and re-establish a vulnerability result for destructive control by deleting candidates. In some of our proofs, we study variants of s-t vertex cuts in graphs that are related to our control problems., Comment: Includes supplemental material

Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this paper we explore the challenging task of geo-entity linking for noisy, multilingual social media data. There are few open-source multilingual geo-entity linking tools available and existing ones are often rule-based, which break easily in social media settings, or LLM-based, which are too expensive for large-scale datasets. We present a method which represents real-world locations as averaged embeddings from labeled user-input location names and allows for selective prediction via an interpretable confidence score. We show that our approach improves geo-entity linking on a global and multilingual social media dataset, and discuss progress and problems with evaluating at different geographic granularities., Comment: NLP+CSS workshop at NAACL 2024

Current content moderation practices follow the trial-and-error approach, meaning that moderators apply sequences of interventions until they obtain the desired outcome. However, being able to preemptively estimate the effects of an intervention would allow moderators the unprecedented opportunity to plan their actions ahead of application. As a first step towards this goal, here we propose and tackle the novel task of predicting the effect of a moderation intervention. We study the reactions of 16,540 users to a massive ban of online communities on Reddit, training a set of binary classifiers to identify those users who would abandon the platform after the intervention - a problem of great practical relevance. We leverage a dataset of 13.8M posts to compute a large and diverse set of 142 features, which convey information about the activity, toxicity, relations, and writing style of the users. We obtain promising results, with the best-performing model achieving micro F1 = 0.800 and macro F1 = 0.676. Our model demonstrates robust generalizability when applied to users from previously unseen communities. Furthermore, we identify activity features as the most informative predictors, followed by relational and toxicity features, while writing style features exhibit limited utility. Our results demonstrate the feasibility of predicting the effects of a moderation intervention, paving the way for a new research direction in predictive content moderation aimed at empowering moderators with intelligent tools to plan ahead their actions.

We present the discovery of large radio shells around a massive pair of interacting galaxies and extended diffuse X-ray emission within the shells. The radio data were obtained with the Australian Square Kilometer Array Pathfinder (ASKAP) in two frequency bands centred at 944 MHz and 1.4 GHz, respectively, while the X-ray data are from the XMM-Newton observatory. The host galaxy pair, which consists of the early-type galaxies ESO 184-G042 and LEDA 418116, is part of a loose group at a distance of only 75 Mpc (redshift z = 0.017). The observed outer radio shells (diameter ~ 145 kpc) and ridge-like central emission of the system, ASKAP J1914-5433 (Physalis), are likely associated with merger shocks during the formation of the central galaxy (ESO 184-G042) and resemble the new class of odd radio circles (ORCs). This is supported by the brightest X-ray emission found offset from the centre of the Physalis system, instead centered at the less massive galaxy, LEDA 418116. The host galaxy pair is embedded in an irregular envelope of diffuse light, highlighting on-going interactions. We complement our combined radio and X-ray study with high-resolution simulations of the circumgalactic medium (CGM) around galaxy mergers from the Magneticum project to analyse the evolutionary state of the Physalis system. We argue that ORCs / radio shells could be produced by a combination of energy release from the central AGN and subsequent lightening up in radio emission by merger shocks traveling through the CGM of these systems., Comment: 12 pages, 8 figures, submitted to MNRAS