Explicit modelling of between-study heterogeneity is essential in network meta-analysis (NMA) to ensure valid inference and avoid overstating precision. While the additive random-effects (RE) model is the conventional approach, the multiplicative-effect (ME) model remains underexplored. The ME model inflates within-study variances by a common factor estimated via weighted least squares, yielding identical point estimates to a fixed-effect model while inflating confidence intervals. We empirically compared RE and ME models across NMAs of two-arm studies with significant heterogeneity from the nmadb database, assessing model fit using the Akaike Information Criterion. The ME model often provided comparable or better fit to the RE model. Case studies further revealed that RE models are sensitive to extreme and imprecise observations, whereas ME models assign less weight to such observations and hence exhibit greater robustness to publication bias. Our results suggest that the ME model warrant consideration alongside conventional RE model in NMA practice.
The so-called Received View (RV) on quantum non-individuality states, basically, that quantum particles are not individuals. It has received an amount of criticism in the recent literature, most of it concerning the relation between the RV and the relation of identity. In this chapter we carefully characterise a family of concepts involved in clarifying the view, indicating how the very idea of failure of identity, commonly used to define the RV, may be understood. By doing so, we hope to dissipate some misunderstandings about the RV, which shall also be seen as evidence of its tenability.
Infrared to millimetre spectral energy distributions have been obtained for 41 bright ultra-luminous infrared galaxies. The observations were carried out with ISOPHOT between 10 and 200 micron and supplemented for 16 sources with SCUBA at 450 and 850 micron and with SEST at 1.3 mm. In addition, seven sources were observed at 1.2 and 2.2 $μ$m with the 2.2 m telescope on Calar Alto. These new SEDs represent the most complete set of infrared photometric templates obtained so far on ULIRGs in the local universe.
Magnetic activity in Sun-like and low-mass stars causes X-ray coronal emission, which is stronger for more rapidly rotating stars. This relation is often interpreted in terms of the Rossby number, i.e., the ratio of rotation period to convective overturn time. We reconsider this interpretation on the basis of the observed X-ray emission and rotation periods of 821 stars with masses below 1.4 Msun. A generalized analysis of the relation between X-ray luminosity normalized by bolometric luminosity, L_X/L_bol, and combinations of rotational period, P, and stellar radius, R, shows that the Rossby formulation does not provide the solution with minimal scatter. Instead, we find that the relation L_X/L_bol ~ P^{-2}R^{-4} optimally describes the non-saturated fraction of the stars. This relation is equivalent to L_X ~ P^{-2}, indicating that the rotation period alone determines the total X-ray emission. Since L_X is directly related to the magnetic flux at the stellar surface, this means that the surface flux is determined solely by the star's rotation and is independent of other stellar parameters. While a formulation in terms of a Rossby number would be consistent with these results if t
Using the recent INTEGRAL/IBIS and Swift/BAT surveys we have extracted a sample of 64 confirmed plus 3 candidate radio galaxies selected in the soft gamma-ray band. The sample covers all optical classes and is dominated by objects showing a FR II radio morphology; a large fraction (70%) of the sample is made of radiative mode or High Excitation Radio Galaxies (HERG). We have measured the source size on NVSS, FIRST and SUMSS images and have compared our findings with data in the literature obtaining a good match. We surprisingly found that the soft gamma-ray selection favours the detection of large size radio galaxies: 60% of objects in the sample have size greater than 0.4 Mpc while around 22% reach dimension above 0.7 Mpc at which point they are classified as Giant Radio Galaxies or GRGs, the largest and most energetic single entities in the Universe. Their fraction among soft gamma ray selected radio galaxies is significantly larger than typically found in radio surveys, where only a few percent of objects (1-6%) are GRGs. This may partly be due to observational biases affecting radio surveys more than soft gamma ray surveys, thus disfavouring the detection of GRGs at lower frequ
Following our previous work constraining interacting dark energy (IDE) models, which showed their potential to alleviate the Hubble tension, in this work we investigate the non-linear effects of the IDE scenario favoured by CMB and DESI observations. The implications of IDE for the $S_8$ tension remain unclear, since current weak-lensing and large-scale-structure analyses either exclude highly non-linear scales or model the non-linear regime using prescriptions calibrated within $Λ$CDM. We address this issue by implementing a fully self-consistent IDE pipeline. We perform N-body simulations of the IDE model with a transfer rate $Q=ξ{\cal H}ρ_x$ using a modified implementation of RAMSES. Since the dark matter Euler equation remains unchanged with respect to $Λ$CDM, the interaction can be incorporated through the modified background evolution and an effective time-dependent dark matter particle mass. We find scale-dependent deviations in the quasi-linear and non-linear regimes of the matter power spectrum, together with modifications to the density-field morphology and halo abundance. Our results show that the impact of IDE on quasi-linear and non-linear structure formation cannot be
We introduce a rigorous approach to the study of the symmetry breaking and pattern formation phenomenon for isotropic functionals with local/nonlocal interactions in competition. We consider a general class of nonlocal variational problems in dimension $d\geq 2$, in which an isotropic surface term favouring pure phases competes with an isotropic nonlocal term with power law kernel favouring alternation between different phases. Close to the critical regime in which the two terms are of the same order, we give a rigorous proof of the conjectured structure of global minimizers, in the shape of domains with flat boundary (e.g., stripes or lamellae). The natural framework in which our approach is set and developed is the one of calculus of variations and geometric measure theory. Among others, we detect a nonlocal curvature-type quantity which is controlled by the energy functional and whose finiteness implies flatness for sufficiently regular boundaries. The power of decay of the considered kernels at infinity is $p\geq d+3$ and it is related to pattern formation in synthetic antiferromagnets.
Empirical evidence shows that human behaviour often deviates from game-theoretical rationality. For instance, humans may hold unrealistic expectations about future outcomes. As the evolutionary roots of such biases remain unclear, we investigate here how reasoning abilities and cognitive biases co-evolve using Evolutionary Game Theory. In our model, individuals in a population deploy a variety of unbiased and biased level-k reasoning strategies to anticipate others' behaviour in sequential interactions, represented by the Incremental Centipede Game. Positively biased reasoning strategies have a systematic inference bias towards higher but uncertain rewards, while negatively biased strategies reflect the opposite tendency. We find that selection consistently favours positively biased reasoning, with rational behaviour even going extinct. This bias co-evolves with bounded rationality, as the reasoning depth remains limited in the population. Interestingly, positively biased agents may co-exist with non-reasoning agents, thus pointing to a novel equilibrium. Longer games further promote positively biased reasoning, as they can lead to higher future rewards. The biased reasoning strate
No one should wait until college to get acquainted with core concepts of quantum information. Given the human bias of favouring the familiar over the unknown, early exposure to concepts of quantum information helps learners build stronger appetence for the field, as well as allowing them to develop an intuitive approach to it. In this work, I present an intuitive gamified approach to one of the core concepts in quantum error correction: the stabiliser formalism.
Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP
Using some results coming from human and social sciences, we are working on the still important problem of tailorability inside CSCW systems. Our proposition aims at favouring the dynamic integration of groupware systems in a global and integrated environment that creates a context for their use. This work leads us to define the problem of the inter-activities management. This study helps us to propose a technical solution to this problem and that is realized in the CooLDA platform.
To advance our understanding of the magnetohydrodynamic (MHD) processes in liquid metals, in this paper we propose an approach combining the classical methods in the dynamo theory based on numerical simulations of the partial differential equations governing the evolution of the magnetic field with the statistical methods. In this study, we intend to answer the following ``optimization'' question: Can we find a statistical explanation what makes a flow to favour magnetic field generation in the linear regime (i.e. the kinematic dynamo is considered), where the Lorenz force is neglected? The flow is assumed to be steady and incompressible, and the magnetic field generation is governed by the magnetic induction equation. The behaviour of its solution is determined by the dominant (i.e. with the largest real part) eigenvalue of the magnetic induction operator. Considering an ensemble of 2193 randomly generated flows, we solved the kinematic dynamo problem and performed an attempt to find a correlation between the dominant eigenvalue and the standard quantities used in hydrodynamics -- vorticity and kinetic helicity. We have found that there is no visible relation between the property
Bayesian Neural Networks (BNN) have emerged as a crucial approach for interpreting ML predictions. By sampling from the posterior distribution, data scientists may estimate the uncertainty of an inference. Unfortunately many inference samples are often needed, the overhead of which greatly hinder BNN's wide adoption. To mitigate this, previous work proposed propagating the first and second moments of the posterior directly through the network. However, on its own this method is even slower than sampling, so the propagated variance needs to be approximated such as assuming independence between neural nodes. The resulting trade-off between quality and inference time did not match even plain Monte Carlo sampling. Our contribution is a more principled variance propagation framework based on "spiked covariance matrices", which smoothly interpolates between quality and inference time. This is made possible by a new fast algorithm for updating a diagonal-plus-low-rank matrix approximation under various operations. We tested our algorithm against sampling based MC Dropout and Variational Inference on a number of downstream uncertainty themed tasks, such as calibration and out-of-distributi
We study how groups reach consensus by varying communication network structure and individual incentives. In 342 networks of seven individuals, single opinionated "leaders" can drive decision outcomes, but do not accelerate consensus formation, whereas conflicting opinions slow consensus. While networks with more links reach consensus faster, this advantage disappears under conflict. Unopinionated individuals make choices consistent with a local majority rule combined with "inertia" favouring their previous choice, while opinionated individuals favour their preferred option but yield under high peer or time pressure. Simulations show these individual rules can account for group patterns, and allow rapid consensus while preventing deadlocks.
We show striped pattern formation in the large volume limit for a class of generalized antiferromagnetic local/nonlocal interaction functionals in general dimension previously considered Goldman-Runa and Daneri-Runa and in Giuliani-Lieb-Lebowitz and Giuliani-Seiringer in the discrete setting. In such a model the relative strength between the short range attractive term favouring pure phases and the long range repulsive term favouring oscillations is modulated by a parameter $τ$. For $τ<0$ minimizers are trivial uniform states. It is conjectured that $\forall\,d\geq2$ there exists $0<\barτ\ll1$ such that for all $0<τ\leq\barτ$ and for all $L>0$ minimizers are striped/lamellar patterns. In Daneri-Runa arXiv:1702.07334 the authors prove the above for $L=2kh^*_τ$, where $k\in\N$ and $h^*_τ$ is the optimal period of stripes for a given $0<τ\leq\barτ$. The purpose of this paper is to show the validity of the conjecture for generic $L$.
It is possible to obtain a large Bayes Factor (BF) favoring the null hypothesis when both the null and alternative hypotheses have low likelihoods, and there are other hypotheses being ignored that are much more strongly supported by the data. As sample sizes become large it becomes increasingly probable that a strong BF favouring a point null against a conventional Bayesian vague alternative co-occurs with a BF favouring various specific alternatives against the null. For any BF threshold q and sample mean, there is a value n such that sample sizes larger than n guarantee that although the BF comparing H0 against a conventional (vague) alternative exceeds q, nevertheless for some range of hypothetical μ, a BF comparing H0 against μ in that range falls below 1/q. This paper discusses the conditions under which this conundrum occurs and investigates methods for resolving it.
We report the detection of GJ 3090 b (TOI-177.01), a mini-Neptune on a 2.9-day orbit transiting a bright (K = 7.3 mag) M2 dwarf located at 22 pc. The planet was identified by the Transiting Exoplanet Survey Satellite and was confirmed with the High Accuracy Radial velocity Planet Searcher radial velocities. Seeing-limited photometry and speckle imaging rule out nearby eclipsing binaries. Additional transits were observed with the LCOGT, Spitzer, and ExTrA telescopes. We characterise the star to have a mass of 0.519 $\pm$ 0.013 M$_\odot$ and a radius of 0.516 $\pm$ 0.016 R$_\odot$. We modelled the transit light curves and radial velocity measurements and obtained a planetary mass of 3.34 $\pm$ 0.72 M$_\oplus$, a radius of 2.13 $\pm$ 0.11 R$_\oplus$, and a mean density of 1.89$^{+0.52}_{-0.45}$ g/cm$^3$. The low density of the planet implies the presence of volatiles, and its radius and insolation place it immediately above the radius valley at the lower end of the mini-Neptune cluster. A coupled atmospheric and dynamical evolution analysis of the planet is inconsistent with a pure H-He atmosphere and favours a heavy mean molecular weight atmosphere. The transmission spectroscopy met
A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory: we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles' dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. The tradeoff disappears when the number of particle types reaches a value $m_*$, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a
I present arguments favouring linearity of the intrinsic (heating-free) I-V of the `intrinsic' Josephson effect.