Miracle-dz/UniFAR-FTUs huggingface dataset:
Interplay between Higgs inflation and dark matter models with dark $U(1)$ gauge symmetry

We investigate dark matter phenomenology and Higgs inflation in a dark $U(1)_D$-extended model. The model features two dark matter candidates, a dark fermion and a dark vector boson. When the fermion DM $\psi$ is heavier than the vector DM $W_D$, there is an ample parameter space where $\psi$ is dominant over $W_D$. The model can then easily evade the stringent bounds from direct detection experiments, since $\psi$ has no direct coupling to the Standard Model particles. Furthermore, the model can accommodate inflation in three different ways, one along the Standard Model Higgs direction, one along the dark Higgs direction, and one along the combination of the two. Considering the running of the parameters and various observational constraints, we perform a detailed numerical analysis and identify allowed parameter spaces that explain both dark matter and Higgs inflation in a unified manner. We discuss in detail how the imposition of Higgs inflation severely constrains the dark matter parameter space. The existence of the dark Higgs field is found to play a crucial role both in dark matter phenomenology and in generalised Higgs inflation.
A divergence free C0-RIPG stream function formulation of the incompressible Stokes system with variable viscosity

Pointwise divergence free velocity field approximations of the Stokes system are gaining popularity due to their necessity in precise modelling of physical flow phenomena. Several methods have been designed to satisfy this requirement; however, these typically come at a greater cost when compared with standard conforming methods, for example, because of the complex implementation and development of specialized finite element bases. Motivated by the desire to mitigate these issues for 2D simulations, we present a $C^0$-interior penalty Galerkin (IPG) discretization of the Stokes system in the stream function formulation. In order to preserve a spatially varying viscosity this approach does not yield the standard and well known biharmonic problem. We further employ the so-called robust interior penalty Galerkin (RIPG) method; stability and convergence analysis of the proposed scheme is undertaken. The former, which involves deriving a bound on the interior penalty parameter is particularly useful to address the $\mathcal{O}(h^{-4})$ growth in the condition number of the discretized operator. Numerical experiments confirming the optimal convergence of the proposed method are undertaken. Comparisons with thermally driven buoyancy mantle convection model benchmarks are presented.
Disinformation Echo-Chambers on Facebook

The landscape of information has experienced significant transformations with the rapid expansion of the internet and the emergence of online social networks. Initially, there was optimism that these platforms would encourage a culture of active participation and diverse communication. However, recent events have brought to light the negative effects of social media platforms, leading to the creation of echo chambers, where users are exposed only to content that aligns with their existing beliefs. Furthermore, malicious individuals exploit these platforms to deceive people and undermine democratic processes. To gain a deeper understanding of these phenomena, this chapter introduces a computational method designed to identify coordinated inauthentic behavior within Facebook groups. The method focuses on analyzing posts, URLs, and images, revealing that certain Facebook groups engage in orchestrated campaigns. These groups simultaneously share identical content, which may expose users to repeated encounters with false or misleading narratives, effectively forming"disinformation echo chambers. "This chapter concludes by discussing the theoretical and empirical implications of these findings.
IS LODGEPOLE PINE MORTALITY DUE TO MOUNTAIN PINE BEETLE LINKED TO THE NORTH AMERICAN MONSOON

Regional precipitation patterns may have influenced the spatial variability of tree mortality during the recent mountain pine beetle (Dendroctonus ponderosa) (MPB) outbreak in the western United States. Data from the Forest Inventory and Analysis (FIA) Program show that the outbreak was especially severe in the state of Colorado where over 10 million lodgepole pines (Pinus contorta Dougl. Ex Loud.) succumbed to MPB between 2002 and 2009. Aerial detection maps of MPB-related mortality show that the infestation was initially widespread and evenly distributed throughout the range of lodgepole pine in Colorado, but gradually became more severe in the northern portion of the state. Because southern Colorado receives relatively high summer precipitation due to the effects of the North American monsoon (NAM), the spatial pattern of MPB-related mortality suggests that infestation severity was lower in areas with the higher summer precipitation. This study investigated the link between lodgepole pine mortality due to MPB and seasonal precipitation patterns associated with the NAM in Colorado. Data regarding insect-related tree mortality and damage data were summarized from FIA data collected between 2002 and 2009, and gridded precipitation data were acquired from the North American Regional Reanalysis Project. Results indicated that while absolute NAM-related precipitation was not an important predictor of infestation severity, the deviation of a five-year average of summer and fall precipitation relative to climatic means was important.
Efficient circular Dyson Brownian motion algorithm

Circular Dyson Brownian motion describes the equilibrium and non-equilibrium level dynamics of unitary ("circular") matrices within the log-gas picture of random matrix theory. A common scenario is that one wants to know about a spectrum evolved over a certain interval of time, without being interested in the intermediate dynamics. Numerical evaluation of this is computationally expensive as the time-evolution algorithm is accurate only on short time intervals because of an underlying perturbative approximation, leading to the need of extensively many intermediate evaluations. This work proposes an efficient and easy-to-implement improved circular Dyson Brownian motion algorithm for models with broken time-reversal symmetry. This algorithm can be seen as a generalization of a commonly used algorithm generating samples from the circular unitary ensemble. The algorithm allows one to study time-evolution over arbitrarily large intervals of time at a fixed computational cost, with no approximations being involved.
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration

In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. In our work, we investigate the use of LLMs to augment training data of smaller language models (SLMs) with automatically generated counterfactual (CF) instances – i.e. minimally altered inputs – in order to improve out-of-domain (OOD) performance of SLMs in the extractive question answering (QA) setup. We show that, across various LLM generators, such data augmentation consistently enhances OOD performance and improves model calibration for both confidence-based and rationale-augmented calibrator models. Furthermore, these performance improvements correlate with higher diversity of CF instances in terms of their surface form and semantic content. Finally, we show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance, indicating that rationale-augmented calibrators prefer concise explanations.
Lidar Ratios for Dust Aerosols Derived From Retrievals of CALIPSO Visible Extinction Profiles Constrained by Optical Depths from MODIS-Aqua and CALIPSO/CloudSat Ocean Surface Reflectance Measurements

CALIPSO's (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) analysis algorithms generally require the use of tabulated values of the lidar ratio in order to retrieve aerosol extinction and optical depth from measured profiles of attenuated backscatter. However, for any given time or location, the lidar ratio for a given aerosol type can differ from the tabulated value. To gain some insight as to the extent of the variability, we here calculate the lidar ratio for dust aerosols using aerosol optical depth constraints from two sources. Daytime measurements are constrained using Level 2, Collection 5, 550-nm aerosol optical depth measurements made over the ocean by the MODIS (Moderate Resolution Imaging Spectroradiometer) on board the Aqua satellite, which flies in formation with CALIPSO. We also retrieve lidar ratios from night-time profiles constrained by aerosol column optical depths obtained by analysis of CALIPSO and CloudSat backscatter signals from the ocean surface.
Exploring the development of a framework of social accountability standards for healthcare service delivery: a qualitative multipart, multimethods process

Objectives Social accountability is an equity-oriented health policy strategy that requires institutions to focus on local population needs. This strategy is well established in health professional education, but there is limited understanding of its application in healthcare service delivery. Building on what is known in the education setting, this study aimed to explore the development of a framework of comprehensive, evidence-based social accountability standards for healthcare service delivery institutions. Design This qualitative, multipart, multimethods study consisted of a modified Delphi process guided by an evidence-based social accountability tool for health professional education and complementary methods including developmental evaluation and a review of select literature to capture emerging evidence and contextual relevance. Setting The study took place in Northern Ontario, Canada at a medical school and a tertiary, regional academic health sciences centre that are both grounded in social accountability. Participants Eight expert participants from diverse, multidisciplinary backgrounds, including a patient advocate, were purposefully recruited from both institutions, enrolled and seven completed the study. Main outcome The resulting framework of social accountability standards is organised into 4 major sections that capture broad and critical concepts; 17 key component reflective questions that address key themes; 39 aspirations that describe objective standards and 197 indicators linked to specific expectations. Results Three modified Delphi rounds were completed producing a framework of consensus derived standards. Developmental evaluation helped identify facilitators, barriers and provided real-time feedback to the study’s processes and content. The literature reviewed identified 10 new concepts and 43 amendments. Conclusion This study highlights the development of a comprehensive, evidence-based framework of social accountability standards for healthcare service delivery institutions. Future studies will aim to evaluate the application of these standards to guide equity-oriented social accountability health policy strategies in healthcare service delivery.
Management of Lateral Wall Insufficiency

Abstract Lateral wall insufficiency is a commonly encountered etiology of nasal obstruction, resulting from dynamic collapse at the level of the internal or external nasal valve. Various management strategies exist to strengthen the lateral wall or stent the nasal valves to relieve nasal obstruction, and range from noninvasive devices, minimally invasive implants, or surgical reconstructive techniques. Surgical options to address the nasal valves are selected based on each patient's anatomic findings, aesthetic and functional goals, and surgeon preference. This article describes the anatomy and physiology of the nasal sidewall and nasal valves and diagnosis of lateral wall insufficiency, and provides a framework for treatment options.
DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks

Generating realistic talking faces is a complex and widely discussed task with numerous applications. In this paper, we present DiffTalker, a novel model designed to generate lifelike talking faces through audio and landmark co-driving. DiffTalker addresses the challenges associated with directly applying diffusion models to audio control, which are traditionally trained on text-image pairs. DiffTalker consists of two agent networks: a transformer-based landmarks completion network for geometric accuracy and a diffusion-based face generation network for texture details. Landmarks play a pivotal role in establishing a seamless connection between the audio and image domains, facilitating the incorporation of knowledge from pre-trained diffusion models. This innovative approach efficiently produces articulate-speaking faces. Experimental results showcase DiffTalker's superior performance in producing clear and geometrically accurate talking faces, all without the need for additional alignment between audio and image features.
Statistically Valid Variable Importance Assessment through Conditional Permutations

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An experiment on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.
Late-time phenomenology required to solve the 
H0
 tension in view of the cosmic ladders and the anisotropic and angular BAO datasets

The $\sim 5\sigma$ mismatch between the value of the Hubble parameter measured by SH0ES and the one inferred from the inverse distance ladder (IDL) constitutes the biggest tension afflicting the standard model of cosmology, which could be pointing to the need of physics beyond $\Lambda$CDM. In this paper we study the background history required to solve the $H_0$ tension if we consider standard prerecombination physics, paying special attention to the role played by the data on baryon acoustic oscillations (BAO) employed to build the IDL. We show that the anisotropic BAO data favor an ultra-late-time (phantom-like) enhancement of $H(z)$ at $z\lesssim 0.2$, accompanied by a transition in the absolute magnitude of supernovae of Type Ia $M(z)$ in the same redshift range. This agrees with previous findings in the literature. The effective dark energy (DE) density must be smaller than in the standard model at higher redshifts. Instead, when angular BAO data (claimed to be less subject to model dependencies) is employed in the analysis, we find that the increase of $H(z)$ starts at much higher redshifts, typically in the range $z\sim 0.5-0.8$. In this case, $M(z)$ could experience also a transition (although much smoother) and the effective DE density becomes negative at $z\gtrsim 2$. Both scenarios require a violation of the weak energy condition (WEC), but leave an imprint on completely different redshift ranges and might also have a different impact on the perturbed observables. They allow for the effective crossing of the phantom divide. Finally, we employ two alternative methods to show that current data from cosmic chronometers do not exclude the violation of the WEC, but do not add any strong evidence in its favor neither. Our work puts the accent on the utmost importance of the choice of the BAO data set in the study of the possible solutions to the $H_0$ tension.
Measuring Higher-Order Rationality with Belief Control

Determining an individual's strategic reasoning capability based solely on choice data is a complex task. This complexity arises because sophisticated players might have non-equilibrium beliefs about others, leading to non-equilibrium actions. In our study, we pair human participants with computer players known to be fully rational. This use of robot players allows us to disentangle limited reasoning capacity from belief formation and social biases. Our results show that, when paired with robots, subjects consistently demonstrate higher levels of rationality and maintain stable rationality levels across different games compared to when paired with humans. This suggests that strategic reasoning might indeed be a consistent trait in individuals. Furthermore, the identified rationality limits could serve as a measure for evaluating an individual's strategic capacity when their beliefs about others are adequately controlled.
Linking Mechanisms: Limits and Robustness

Quota mechanisms are commonly used to elicit private information when agents face multiple decisions and monetary transfers are infeasible. As the number of decisions grows large, quotas asymptotically implement the same set of social choice functions as do separate mechanisms with transfers. We analyze the robustness of quota mechanisms. To set the correct quota, the designer must have precise knowledge of the environment. We show that, without transfers, only trivial social choice rules can be implemented in a prior-independent way. We obtain a tight bound on the decision error that results when the quota does not match the true type distribution. Finally, we show that in a multi-agent setting, quotas are robust to agents' beliefs about each other. Crucially, quotas make the distribution of reports common knowledge.
Sufficient condition for universal quantum computation using bosonic circuits

Continuous-variable bosonic systems stand as prominent candidates for implementing quantum computational tasks. While various necessary criteria have been established to assess their resourcefulness, sufficient conditions have remained elusive. We address this gap by focusing on promoting circuits that are otherwise simulatable to computational universality. The class of simulatable, albeit non-Gaussian, circuits that we consider is composed of Gottesman-Kitaev-Preskill (GKP) states, Gaussian operations, and homodyne measurements. Based on these circuits, we first introduce a general framework for mapping a continuous-variable state into a qubit state. Subsequently, we cast existing maps into this framework, including the modular and stabilizer subsystem decompositions. By combining these findings with established results for discrete-variable systems, we formulate a sufficient condition for achieving universal quantum computation. Leveraging this, we evaluate the computational resourcefulness of a variety of states, including Gaussian states, finite-squeezing GKP states, and cat states. Furthermore, our framework reveals that both the stabilizer subsystem decomposition and the modular subsystem decomposition (of position-symmetric states) can be constructed in terms of simulatable operations. This establishes a robust resource-theoretical foundation for employing these techniques to evaluate the logical content of a generic continuous-variable state, which can be of independent interest.
Transparently Serving the Public: Enhancing Public Service Media Values through Exploration

In the last few years, we have reportedly underlined the importance of the Public Service Media Remit for ZDF as a Public Service Media provider. Offering fair, diverse, and useful recommendations to users is just as important for us as being transparent about our understanding of these values, the metrics that we are using to evaluate their extent, and the algorithms in our system that produce such recommendations. This year, we have made a major step towards transparency of our algorithms and metrics describing them for a broader audience, offering the possibility for the audience to learn details about our systems and to provide direct feedback to us. Having the possibility to measure and track PSM metrics, we have started to improve our algorithms towards PSM values. In this work, we describe these steps and the results of actively debasing and adding exploration into our recommendations to achieve more fairness.
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work.