Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for agentic settings, where actions directly interact with external environments and their effects can be irreversible and costly. We propose ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, a framework that decouples exploration from commitment by enabling test-time exploration through simulated interactions prior to real-world execution. This design allows extending inference-time computation to improve action-level reliability and robustness without incurring environmental risk. We further show that naive LLM-based simulators struggle to capture rare but high-impact failure modes, substantially limiting their effectiveness for agentic decision making. To address this limitation, we introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions via targeted data generation and rebalanced training. Experiments on multi-turn and multi-step agentic benchmarks demonstrate that iterative simulation substantially improves agent reliability, and that risk-aware simul
The unprecedented statistical power of galaxy cluster catalogs from the SRG (Spectrum Roentgen Gamma)/eROSITA All-Sky Survey provides a unique opportunity to place stringent constraints on cosmological models through measurements of structure growth. Fully exploiting the potential of these large X-ray-selected cluster samples, however, requires robust statistical frameworks that accurately connect observable quantities to the underlying cosmological parameters. We develop and implement a simulation-based inference (SBI) framework for cosmological parameter estimation using a realistic mock-generation pipeline calibrated on eRASS1 simulations. Synthetic galaxy cluster catalogs are propagated through the survey selection function to produce mock eRASS1 observations that reproduce the data's statistical properties. At the core of the method lies a set-based neural network (GNN on sets) that encodes information from individual clusters and is coupled to a masked autoregressive flow for flexible posterior density estimation. This approach enables the use of the full cluster-level information content without compressing the observables into binned summary statistics. Our framework recove
The world is full of systems of distributed agents, collaborating and competing in complex ways: firms and workers specialise within economies, neurons adapt their tuning across brain circuits, and species compete and coexist within ecosystems. In that context, individual research fields built theories explaining how comparative advantage drives trade specialisation, how balanced neural representations emerge from sensory coding, and how biodiversity sustains ecological productivity. Here we propose that many of these well-understood findings across fields can be captured in one simple joint cross-disciplinary model, which we call the Distributed Production System. It captures how agent heterogeneity, resource constraints, communication topology, and task structure jointly determine the productivity, efficiency, and robustness of distributed systems across biology, economics, neuroscience, and computing. This model reveals that a small set of underlying laws generates the complex dynamics observed across fields. These can be summarised in our Principle of Maximum Heterogeneity: any distributed production system optimising for performance will converge on an increasingly heterogeneo
We present the weak-lensing mass calibration and constrain the BCG (brightest cluster galaxy) stellar-mass-to-halo-mass-and-redshift ($M_{\star,\mathrm{BCG}}-M-z$) relation for a sample of $124$ galaxy clusters and groups at redshift $0.1<z<0.8$ from the first Data Release of the $eROSITA$ All-Sky Survey (eRASS1), using data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The cluster survey is conducted by the $eROSITA$ X-ray telescope aboard the Spectrum-Roentgen-Gamma (SRG) space observatory. The cluster sample is X-ray-selected and optically confirmed with a negligibly low contamination rate ($\approx5%$). On a basis of individual clusters, the shear profiles of $96$ clusters are derived using the HSC Three-Year (HSC-Y3) weak-lensing data, while the BCG stellar masses of $101$ clusters are estimated using the SED template fitting to the HSC five-band ($grizY$) photometry. The observed X-ray photon count rate is used as the mass proxy, based on which individual halo masses are obtained at the given count rate in a population modelling while accounting for systematic uncertainties in the weak-lensing modelling through a simulation-calibrated weak-lensing mass-to-h
The detection of the extended X-ray-emission of the intracluster medium by the first SRG/eROSITA All-Sky Survey (eRASS1), combined with optical and near-infrared follow-up, resulted in the identification of more than 12000 galaxy clusters, yielding precise constraints on cosmological parameters. However, some clusters of galaxies can be misclassified as point sources by eROSITA's source detection algorithm due to the interplay between the point-spread function, the shallow depth of the survey, compact (cool core) X-ray emission, and bright active galactic nuclei hosted in their centers or their vicinity. To identify such misclassified galaxy clusters and groups, we apply optical follow-up to the eRASS1 X-ray point sources analogously to the treatment of the extent-selected catalog. After rigorous filtering to ensure purity, we find a total of 8347 clusters of galaxies, of which 5819 are novel detections, in a redshift range $0.05 < z \lesssim 1.1$. This corresponds to a 70 % discovery rate, a fraction similar to that of the extent-selected sample. To facilitate finding new exceptional clusters such as the Phoenix cluster (which is recovered in our sample), we divide the clusters
The soft X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) mission, eROSITA (extended ROentgen Survey with an Imaging Telescope Array), has produced the largest sample to date of galaxy groups and clusters detected via their intracluster/intragroup medium (ICM/IGrM) emission. Scaling relations between the intrinsic properties of these systems provide valuable insight into their formation and evolution. In this work, we investigate the scaling relations between key physical properties, such as soft band X-ray luminosity, temperature, gas mass, and the low-scatter mass proxy $Y_{\rm X}$, for the galaxy groups and clusters detected in the first eROSITA All-Sky Survey (eRASS1). Our analysis fully accounts for selection effects and the redshift evolution of the observable distributions. We construct a high-purity sample of $3061$ galaxy groups and clusters spanning the redshift range $0.05<z<1.07$ and mass range of $1.1\times10^{13}<M_{500}/$M$_{\odot}<1.6\times10^{15}$. This represents the largest sample to date used for scaling relation analysis. The selection function, derived from state-of-the-art simulations of the eROSITA sky, is rigorously incorporated into o
We propose ARTI-6, a compact six-dimensional articulatory speech encoding framework derived from real-time MRI data that captures crucial vocal tract regions including the velum, tongue root, and larynx. ARTI-6 consists of three components: (1) a six-dimensional articulatory feature set representing key regions of the vocal tract; (2) an articulatory inversion model, which predicts articulatory features from speech acoustics leveraging speech foundation models, achieving a prediction correlation of 0.87; and (3) an articulatory synthesis model, which reconstructs intelligible speech directly from articulatory features, showing that even a low-dimensional representation can generate natural-sounding speech. Together, ARTI-6 provides an interpretable, computationally efficient, and physiologically grounded framework for advancing articulatory inversion, synthesis, and broader speech technology applications. The source code and speech samples are publicly available.
Ultralight axions are hypothetical scalar particles that influence the evolution of large-scale structures of the Universe. Depending on their mass, they can potentially be part of the dark matter component of the Universe as candidates commonly referred to as fuzzy dark matter. While strong constraints have been established for pure fuzzy dark matter models, the more general scenario where ultralight axions constitute only a fraction of the dark matter has been limited to only a few observational probes. In this work, we use the galaxy cluster number counts obtained from the first All-Sky Survey (eRASS1) of the SRG/eROSITA mission together with gravitational weak lensing data from the Dark Energy Survey, the Kilo-Degree Survey, and the Hyper Suprime-Cam to constrain the fraction of ultralight axions in the mass range $10^{-32}$ eV to $10^{-24}$ eV. We put upper bounds on the ultralight axion relic density $Ω_\mathrm{a}$ in independent logarithmic axion mass bins by performing a full cosmological parameter inference. We find an exclusion region in the intermediate ultralight axion mass regime with the tightest bounds reported so far in the mass bins around $m_\text{a} = 10^{-27}$ e
The hot gas in the outskirts of galaxy cluster-sized halos, extending around and beyond the virial radius into nearby accretion regions, remains among one of the least explored baryon components of the large-scale cosmic structure. We present a stacking analysis of 680 galaxy clusters located in the western Galactic hemisphere, using data from the first two years of the SRG/eROSITA All-Sky Survey. The stacked X-ray surface brightness (SB) profile reveals a statistically significant signal extending out to 2 r_200m (~4.5 Mpc). The best-fit SB profile is well described by a combination of terms describing orbiting and infalling gas, with a transition occurring around r_200m. At this radius, the gas number density corresponds to a baryon overdensity of about 30. By integrating the density profile out to r_200m, we inferred a gas fraction exceeding the universal baryon fraction, assuming a typical halo concentration. However, correcting for possible clumping effects reduces the baryon fraction by more than 20%. Additionally, we examined the distribution of hot gas in massive clusters in the IllustrisTNG simulations, from the halo center to the accretion region. This analysis reveals di
In this paper, we model the locations of five major banks in mainland France, two lucrative and three cooperative institutions based on socio-economic considerations. Locations of banks are collected using web scrapping and constitute a bivariate spatial point process for which we estimate nonparametrically summary functions (intensity, Ripley and cross-Ripley's K functions). This shows that the pattern is highly inhomogenenous and exhibits a clustering effect especially at small scales, and thus a significant departure to the bivariate (inhomogeneous) Poisson point process is pointed out. We also collect socio-economic datasets (at the living area level) from INSEE and propose a parametric modelling of the intensity function using these covariates. We propose a group-penalized bivariate composite likelihood method to estimate the model parameters, and we establish its asymptotic properties. The application of the methodology to the banking dataset provides new insights into the specificity of the cooperative model within the sector, particularly in relation to the theories of institutional isomorphism.
We explore halo assembly bias on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we construct two subsamples of galaxy clusters which consist of supercluster members (SC) and isolated clusters (ISO) respectively. After correcting the selection effects on redshift, mass, and survey depth, we compute the excess in the concentration of the intracluster gas of isolated clusters with respect to supercluster members, defined as $δc_{\rm gas} \equiv c_{\rm gas,ISO}/c_{\rm gas,SC}-1$, to investigate the environmental effect on the concentration of clusters, an inference of halo assembly bias on cluster scales. We find that the average gas mass concentration of isolated clusters is a few percent higher than that of supercluster members, with a maximum significance of $2.8σ$. The result on $δc_{\rm gas}$ varies with the overdensity ratio $f$ in supercluster identification, cluster mass proxies, and mass ranges, but remains positive in all the measurements. We measure slightly larger $δc_{\rm gas}$ when adopting a higher $f$ in supercluster identification. $δc_{\r
The cluster mass function traces the growth of linear density perturbations and provides valuable insights into the growth of structures, the nature of dark matter, and the cosmological parameters governing the Universe. The primary science goal of eROSITA, on board the {\it Spectrum Roentgen Gamma (SRG)} mission, launched in 2019, is to constrain cosmology through the evolution of cluster mass function. In this paper, we present the cosmological constraints obtained from 5259 clusters of galaxies detected over an area of 12791~deg$^2$ in the Western Galactic Hemisphere of the eROSITA's first All-Sky Survey (eRASS1). The common footprint region between the eROSITA Survey and DES, KiDS, and HSC surveys is used for calibration of the scaling between X-ray count rate and their total mass through measurements of their weak gravitational lensing signal. eRASS1 cluster abundances constrain the $Λ$CDM parameters, which are the energy density of the total matter to $Ω_{\mathrm{m}}=0.29^{+0.01}_{-0.02}$, and the normalization of the density fluctuations to $σ_8=0.88\pm0.02$ and their combination yields $S_8=σ_8 (Ω_\mathrm{m} / 0.3)^{0.5}=0.86\pm0.01$, consistent and at a similar precision w
Clusters of galaxies can be used as powerful probes to study astrophysical processes on large scales, test theories of the growth of structure, and constrain cosmological models. The driving science goal of the SRG/eROSITA All-Sky Survey (eRASS) is to assemble a large sample of X-ray-selected clusters with a well-defined selection function to determine the evolution of the mass function and, hence, the cosmological parameters. We present here a catalog of 12247 optically confirmed galaxy groups and clusters detected in the 0.2-2.3 keV as extended X-ray sources in a 13,116deg$^2$ region in the western Galactic hemisphere of the sky, which eROSITA surveyed in its first six months of operation. The clusters in the sample span the redshift range $0.003<z<1.32$. The majority (68%) of these clusters, 8361 sources, represent new discoveries without known counterparts in the literature. The mass range of the sample covers three orders of magnitude from $5\times10^{12}M_{\rm sun}$ to $2\times10^{15}M_{\rm sun}$. We construct a sample for cosmology with a higher purity level (~95%) than the primary sample, comprising 5259 securely detected and confirmed clusters in the 12791deg$^{2}$ c
We aim to participate in the calibration of the X-ray photon count rate to halo mass scaling relation of galaxy clusters selected in the first eROSITA All-Sky Survey on the Western Galactic Hemisphere (eRASS1) using KiDS-1000 weak-lensing (WL) data. We measure the radial shear profiles around eRASS1 galaxy clusters using background galaxies in KiDS-1000, as well as the cluster member contamination. Furthermore we provide consistency checks with the other stage-III WL surveys who take part in the eRASS1 mass calibration, DES Y3 and HSC-Y3. We determine the cluster member contamination of eRASS1 clusters present in KiDS-1000 based on source number density profiles, where we account for the obscuration caused by cluster galaxies. The extracted shear profiles, together with the contamination model and the lens sample selection, are then analysed through a Bayesian population model. We calibrate the WL mass bias parameter by analysing realistic synthetic shear profiles from mock cluster catalogues. Our consistency checks between KiDS-1000 and DES Y3 & HSC-Y3 include the comparison of contamination-corrected density contrast profiles employing the union of background sources around c
We investigate the impact of AGN feedback, on the entropy and characteristic temperature measurements of galaxy groups detected in the SRG/eROSITA's first All-Sky Survey (eRASS1) to shed light on the characteristics of the feedback mechanisms. We analyze deeper eROSITA observations of 1178 galaxy groups detected in eRASS1. We divide the sample into 271 subsamples and extract average thermodynamic properties, including electron density, temperature, and entropy at three characteristic radii along with the integrated temperature by jointly analyzing X-ray images and spectra following a Bayesian approach. We present the tightest constraints on the impact of AGN feedback through our average entropy and characteristic temperature measurements of the largest group sample used in X-ray studies, incorporating major systematics in our analysis. We find that entropy shows an increasing trend with temperature in the form of a power-law-like relation at the higher intra-group medium temperatures, while for the low mass groups, a slight flattening is observed on the average entropy. Overall, the observed entropy measurements agree well with the earlier measurements in the literature. The compar
The first SRG/eROSITA All-Sky Survey (eRASS1) provides the largest intracluster medium-selected galaxy cluster and group catalog covering the western galactic hemisphere. Compared to samples selected purely on X-ray extent, the sample purity can be enhanced by identifying cluster candidates using optical and near-infrared data from the DESI Legacy Imaging Surveys. Using the red-sequence-based cluster finder eROMaPPer, we measured individual photometric properties (redshift $z_λ$, richness $λ$, optical center, and BCG position) for 12,000 eRASS1 clusters over a sky area of 13,116 deg$^2$, augmented by 247 cases identified by matching the candidates with known clusters from the literature. The median redshift of the identified eRASS1 sample is $z=0.31$, with 10% of the clusters at $z>0.72$. The photometric redshifts have an accuracy of $δz/(1+z)<0.005$ for $0.05<z<0.9$. Spectroscopic cluster properties (redshift $z_{\rm spec}$ and velocity dispersion $σ$) are measured a posteriori for a subsample of 3,210 and 1,499 eRASS1 clusters, respectively, using an extensive compilation of spectroscopic redshifts of galaxies from the literature. We infer that the primary eRASS1 samp
This paper presents an experimental study on radio frequency (RF) fingerprinting of Bluetooth Classic devices. Our research aims to provide a practical evaluation of the possibilities for RF fingerprinting of everyday Bluetooth connected devices that may cause privacy risks. We have built an experimental setup for recording Bluetooth connection in a radio frequency isolated environment using commercially available SDR (software defined radio) systems, extracted fingerprints of the Bluetooth radio data in the form of carrier frequency offset and scaling factor from 6 different devices, and performed k-nearest neighbors (kNN) classification achieving 84\% accuracy. The experiment demonstrates that no matter what privacy measures are being taken in the protocol layer, the physical layer leaks significant information about the device to unauthorized listeners. In the context of the ever-growing Bluetooth device market, this research serves as a clarion call for device manufacturers, regulators, and end-users to acknowledge the privacy risks posed by RF fingerprinting and lays a foundation for more sizeable Bluetooth fingerprinting analysis research.
Beyond testing the current cosmological paradigm, cluster number counts can also be utilized to investigate the discrepancies currently affecting current cosmological measurements. In particular, cosmological studies based on cosmic shear and other large-scale structure probes routinely find a value of the amplitude of the fluctuations in the universe S8 smaller than the one inferred from the primary cosmic microwave background. In this work, we investigate this tension by measuring structure evolution across cosmic time as probed by the number counts of the massive halos with the first SRG/eROSITA All-Sky Survey cluster catalog in the Western Galactic Hemisphere complemented with the overlapping Dark Energy Survey Year-3, KiloDegree Survey, and Hyper Suprime-Cam data for weak lensing mass calibration, by implementing two different parameterizations and a model-agnostic method. In the first model, we measure the cosmic linear growth index as γ = 1.19 \pm 0.21, in tension with the standard value of γ = 0.55, but in good statistical agreement with other large-scale structures probes. The second model is a phenomenological scenario in which we rescale the linear matter power spectrum
The evolution of the cluster mass function traces the growth of the linear density perturbations and can be utilized for constraining the parameters of cosmological and alternative gravity models. In this context, we present new constraints on potential deviations from general relativity by investigating the Hu-Sawicki parametrization of the f(R) gravity with the first SRG-eROSITA All-Sky Survey (eRASS1) cluster catalog in the Western Galactic Hemisphere in combination with the overlapping Dark Energy Survey Year 3, KiloDegree Survey and Hyper Supreme Camera data for weak lensing mass calibration. For the first time, we present constraints obtained from cluster abundances only. When we consider massless neutrinos, we find a strict upper limit of log |fR0| < -4.31 at 95% confidence level. Massive neutrinos suppress structure growth at small scales, and thus have the opposite effect of f(R) gravity. We consequently investigate the joint fit of the mass of the neutrinos with the modified gravity parameter. We obtain log |fR0| < -4.12 jointly with \sum m_ν< 0.44 e.V. at 95% confidence level, tighter than the limits in the literature utilizing cluster counts only. At log |fR0|=
The acquisition of substantial volumes of 3D articulated object data is expensive and time-consuming, and consequently the scarcity of 3D articulated object data becomes an obstacle for deep learning methods to achieve remarkable performance in various articulated object understanding tasks. Meanwhile, pairing these object data with detailed annotations to enable training for various tasks is also difficult and labor-intensive to achieve. In order to expeditiously gather a significant number of 3D articulated objects with comprehensive and detailed annotations for training, we propose Articulated Object Procedural Generation toolbox, a.k.a. Arti-PG toolbox. Arti-PG toolbox consists of i) descriptions of articulated objects by means of a generalized structure program along with their analytic correspondence to the objects' point cloud, ii) procedural rules about manipulations on the structure program to synthesize large-scale and diverse new articulated objects, and iii) mathematical descriptions of knowledge (e.g. affordance, semantics, etc.) to provide annotations to the synthesized object. Arti-PG has two appealing properties for providing training data for articulated object und