Miracle-dz/UniFAR-FTUs huggingface dataset:
Ejective and preventative: the IllustrisTNG black hole feedback and its effects on the thermodynamics of the gas within and around galaxies

Supermassive black holes (SMBHs) which reside at the centres of galaxies can inject vast amounts of energy into the surrounding gas and are thought to be a viable mechanism to quench star-formation in massive galaxies. Here we study the $10^{9-12.5}\,\mathrm{M_\odot}$ stellar mass central galaxy population of the IllustrisTNG simulation, specifically the TNG100 and TNG300 volumes at $z=0$, and show how the three components - SMBH, galaxy, and circumgalactic medium (CGM) - are interconnected in their evolution. We find that gas entropy is a sensitive diagnostic of feedback injection. In particular, we demonstrate how the onset of the low-accretion BH feedback mode, realised in the IllustrisTNG model as a kinetic, BH-driven wind, leads not only to star-formation quenching at stellar masses $\gtrsim10^{10.5}\,\mathrm{M_\odot}$ but also to a change in thermodynamic properties of the (non-star-forming) gas, both within the galaxy and beyond. The IllustrisTNG kinetic feedback from SMBHs increases the average gas entropy, within the galaxy and in the CGM, lengthening typical gas cooling times from $10-100\, \mathrm{Myr}$ to $1-10\,\mathrm{Gyr}$, effectively ceasing ongoing star-formation and inhibiting radiative cooling and future gas accretion. In practice, the same AGN feedback channel is simultaneously `ejective' and `preventative' and leaves an imprint on the temperature, density, entropy, and cooling times also in the outer reaches of the gas halo, up to distances of several hundred kiloparsecs. In the IllustrisTNG model, a long-lasting quenching state can occur for a heterogeneous CGM, whereby the hot and dilute CGM gas of quiescent galaxies contains regions of low-entropy gas with short cooling times.
Function Spaces under Model Uncertainty: Order and Aggregation

In robust finance, Knightian uncertainty is often captured by a non-dominated set of priors, probability measures on the future states of the world. This usually comes at the cost of losing tractability; in contrast to the dominated setting, advanced functional analytic tools are often not available anymore. Thus additional assumptions are made to gain a certain degree of tractability in particular with respect to aggregation of families of consistent random variables. We investigate from a reverse perspective the implications of such assumptions.
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines

Many signal processing and machine learning applications are built from evaluating a kernel on pairs of signals, e.g. to assess the similarity of an incoming query to a database of known signals. This nonlinear evaluation can be simplified to a linear inner product of the random Fourier features of those signals: random projections followed by a periodic map, the complex exponential. It is known that a simple quantization of those features (corresponding to replacing the complex exponential by a different periodic map that takes binary values, which is appealing for their transmission and storage), distorts the approximated kernel, which may be undesirable in practice. Our take-home message is that when the features of only one of the two signals are quantized, the original kernel is recovered without distortion; its practical interest appears in several cases where the kernel evaluations are asymmetric by nature, such as a client-server scheme. Concretely, we introduce the general framework of asymmetric random periodic features, where the two signals of interest are observed through random periodic features: random projections followed by a general periodic map, which is allowed to be different for both signals. We derive the influence of those periodic maps on the approximated kernel, and prove uniform probabilistic error bounds holding for all signal pairs from an infinite low-complexity set. Interestingly, our results allow the periodic maps to be discontinuous, thanks to a new mathematical tool, i.e. the mean Lipschitz continuity. We then apply this generic framework to semi-quantized kernel machines (where only one signal has quantized features and the other has classical random Fourier features), for which we show theoretically that the approximated kernel remains unchanged (with the associated error bound), and confirm the power of the approach with numerical simulations.
Galaxy pairs in the Sloan Digital Sky Survey – XIV. Galaxy mergers do not lie on the fundamental metallicity relation

In recent observational studies, star-forming galaxies have been shown to follow a relation often dubbed the fundamental metallicity relation (FMR). This relation links the stellar mass of a galaxy with its star formation rate (SFR) and its gas-phase metallicity. Specifically, the FMR predicts that galaxies, at a given stellar mass, exhibit lower metallicities for higher SFRs. This trend is qualitatively consistent with observations of galaxy pairs, which have been robustly shown to experience increasing gas-phase metallicity dilution and enhanced star formation activity with decreasing projected separation. In this work, we show that, despite the qualitative consistency with FMR expectations, the observed O/H dilution in galaxy pairs of the Sloan Digital Sky Survey is stronger than what is predicted by the FMR. We conclude that the evolutionary phase of galaxies interacting with companions is not encoded in the FMR, and thus, mergers constitute a clearly defined population of outliers. We find that galaxies in pairs are consistent with the FMR only when their separation is larger than 110 kpc. Finally, we also quantify the local environment of the pairs using the number of galaxy neighbours within $2\, \mathrm{Mpc}$, $N_2$, and the projected separation to the second closest galaxy, $r_2$. We find that pairs are more sensitive to a second companion than to the local galaxy density, displaying less elevated SFRs with smaller values of $r_2$.
QED corrections to the 
g
 factor of Li- and B-like ions

QED corrections to the $g$ factor of Li-like and B-like ions in a wide range of nuclear charges are presented. Many-electron contributions as well as radiative effects on the one-loop level are calculated. Contributions resulting from the interelectronic interaction, the self-energy effect, and most of the terms of the vacuum-polarization effect are evaluated to all orders in the nuclear coupling strength $Z\alpha$. Uncertainties resulting from nuclear size effects, numerical computations, and uncalculated effects are discussed.
TOI-1235 b: A Keystone Super-Earth for Testing Radius Valley Emergence Models around Early M Dwarfs

Small planets on close-in orbits tend to exhibit envelope mass fractions of either effectively zero or up to a few percent depending on their size and orbital period. Models of thermally driven atmospheric mass loss and of terrestrial planet formation in a gas-poor environment make distinct predictions regarding the location of this rocky/nonrocky transition in period–radius space. Here we present the confirmation of TOI-1235 b (P = 3.44 days, ), a planet whose size and period are intermediate between the competing model predictions, thus making the system an important test case for emergence models of the rocky/nonrocky transition around early M dwarfs (Rs = 0.630 ± 0.015 , Ms = 0.640 ± 0.016 ). We confirm the TESS planet discovery using reconnaissance spectroscopy, ground-based photometry, high-resolution imaging, and a set of 38 precise radial velocities (RVs) from HARPS-N and HIRES. We measure a planet mass of , which implies an iron core mass fraction of % in the absence of a gaseous envelope. The bulk composition of TOI-1235 b is therefore consistent with being Earth-like, and we constrain an H/He envelope mass fraction to be <0.5% at 90% confidence. Our results are consistent with model predictions from thermally driven atmospheric mass loss but not with gas-poor formation, suggesting that the former class of processes remains efficient at sculpting close-in planets around early M dwarfs. Our RV analysis also reveals a strong periodicity close to the first harmonic of the photometrically determined stellar rotation period that we treat as stellar activity, despite other lines of evidence favoring a planetary origin ( days, ) that cannot be firmly ruled out by our data.
An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification

Anti-spoofing for automatic speaker verification is now a well established area of research, with three competitive challenges having been held in the last 6 years. A great deal of research effort over this time has been invested into the development of front-end representations tailored to the spoofing detection task. One such approach known as constant Q cepstral coefficients (CQCCs) have been shown to be especially effective in detecting attacks implemented with a unit selection based speech synthesis algorithm. Despite their success, they largely fail in detecting other forms of spoofing attack where more traditional front-end representations give substantially better results. Similar differences were also observed in the most recent, 2019 edition of the ASVspoof challenge series. This paper reports our attempts to help explain these observations. The explanation is shown to lie in the level of attention paid by each front-end to different sub-band components of the spectrum. Thus far, surprisingly little has been learned about what artefacts are being detected by spoofing countermeasures. Our work hence aims to shed light upon signal or spectrum level artefacts that serve to distinguish different forms of spoofing attack from genuine, bone fide speech. With a better understanding of these artefacts we will be better positioned to design more reliable countermeasures.
Bounds on the Secrecy Outage Probability for Dependent Fading Channels

The amount of sensitive data, which is transmitted wirelessly will increase with future technologies. This raises many questions about secure data transmission. Besides cryptography, information-theoretic security gained increasing attention over the recent years. Among others, it deals with the problem of secure data transmission on the physical layer to a legitimate receiver (Bob) in the presence of an eavesdropper (Eve). In this work, we investigate upper and lower bounds on the secrecy outage probability for slowly-fading wiretap channels with an arbitrary dependency structure between the fading channels to Bob and Eve. Both cases of absence of channel-state information at the transmitter (CSI-T) and availability of CSI-T of only the main channel to the legitimate receiver are considered. Furthermore, we derive explicit expressions for the upper and lower bounds for Rayleigh fading and compare them to the case of independent channels. The joint distribution of the legitimate and eavesdropper channels has a tremendous impact on the achievable secrecy outage probability. The bounds enable developing guaranteed secrecy schemes by only measuring the marginal channel distributions.
Deep Learning Models for Multilingual Hate Speech Detection

Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with logistic regression performs the best, while in high resource setting BERT based models perform better. In case of zero-shot classification, languages such as Italian and Portuguese achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks. We have made our code and experimental settings public for other researchers at this https URL.
Habitat use and feeding behaviour of the buffalo and the white rhinoceros in the Hluhluwe-Umfolozi Game Reserve

Resource partitioning by buffalo (Syncerus caffer) and white rhinoceros (Ceratotherium simum) was compared during the dry season in the Hluhluwe section of Hluhluwe-Umfolozi Park, KwaZulu-Natal, South Africa. High buffalo densities had been perceived as a potential competitive threat for grazing with co-existing populations of white rhino which have a high conservation status. Feeding behaviour was compared at different scales to identify exploitative competition or facilitation between the two study species; variables considered were use of grass species, grass height, and habitat preference. No significant differences were observed in the use of the common grasses Themeda triandra and Panicum maximum. However, resource partitioning occurred through use of grass of different height. Habitat facilitation was inferred from the observed interaction, since buffalo grazed grass to heights optimal for use by white rhino. Partitioning also occurred with reference to topography and gradient; white rhino occurred in valley bottoms, whereas buffalo displayed an even distribution across the catena. Resources were abundant during the study and no competition was evident between buffalo and white rhino. Management of the area optimises resource use.
Random connection models in the thermodynamic regime: central limit theorems for add-one cost stabilizing functionals

The paper deals with a random connection model, a random graph whose vertices are given by a homogeneous Poisson point process on $\R^d$, and edges are independently drawn with probability depending on the locations of the two end points. We establish central limit theorems (CLT) for general functionals on this graph under minimal assumptions that are a combination of the weak stabilization for the-one cost and a $(2+\delta)$-moment condition. As a consequence, CLTs for isomorphic subgraph counts, isomorphic component counts, the number of connected components, and Betti numbers are then derived.
Almansi-type theorems for slice-regular functions on Clifford algebras

We present an Almansi-type decomposition for polynomials with Clifford coefficients, and more generally for slice-regular functions on Clifford algebras. The classical result by Emilio Almansi, published in 1899, dealt with polyharmonic functions, the elements of the kernel of an iterated Laplacian. Here, we consider polynomials of the form , with Clifford coefficients , and get an analogous decomposition related to zonal polyharmonics. We show the relation between such decomposition and the Dirac (or Cauchy–Riemann) operator and extend the results to slice-regular functions.
Hadron resonance gas with van der Waals interactions

An overview of a hadron resonance gas (HRG) model that includes van der Waals interactions between hadrons is presented. Applications of the excluded volume HRG model to heavy-ion collision data and lattice QCD equation of state are discussed. A recently developed quantum van der Waals hadron resonance gas model is covered as well. Applications of this model in the context of the QCD critical point are elaborated.
Monopoles and Landau-Ginzburg Models I

The end point of this series of papers is to construct the monopole Floer homology for 3-manifolds with torus boundary. In the first paper, we explain the idea from the standpoint of gauged Landau-Ginzburg models and address a few model problems related to the compactness of moduli spaces, using a Bochner-type formula associated to the gauged Witten equations.
What’s so special about BERT’s layers? A closer look at the NLP pipeline in monolingual and multilingual models

Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To investigate to what extent these results also hold for a language other than English, we probe a Dutch BERT-based model and the multilingual BERT model for Dutch NLP tasks. In addition, through a deeper analysis of part-of-speech tagging, we show that also within a given task, information is spread over different parts of the network and the pipeline might not be as neat as it seems. Each layer has different specialisations, so that it may be more useful to combine information from different layers, instead of selecting a single one based on the best overall performance.
Oversampling for Imbalanced Time Series Data

Many important real-world applications involve time-series data with skewed distribution. Compared to conventional imbalance learning problems, the classification of imbalanced time-series data is more challenging due to high dimensionality and high inter-variable correlation. This paper proposes a structure preserving Oversampling method to combat the High-dimensional Imbalanced Time-series classification (OHIT). OHIT first leverages a density-ratio based shared nearest neighbor clustering algorithm to capture the modes of minority class in high-dimensional space. It then for each mode applies the shrinkage technique of large-dimensional covariance matrix to obtain accurate and reliable covariance structure. Finally, OHIT generates the structure-preserving synthetic samples based on multivariate Gaussian distribution by using the estimated covariance matrices. Experimental results on several publicly available time-series datasets (including unimodal and multi-modal) demonstrate the superiority of OHIT against the state-of-the-art oversampling algorithms in terms of F-value, G-mean, and AUC.
Relation Transformer Network

The identification of objects in an image, together with their mutual relationships, can lead to a deep understanding of image content. Despite all the recent advances in deep learning, in particular, the detection and labeling of visual object relationships remain a challenging task. In this work, we present the Relation Transformer Network, which is a customized transformer-based architecture that models complex object to object and edge to object interactions, by taking into account global context. Our hierarchical multi-head attention-based approach efficiently models and predicts dependencies between objects and their contextual relationships. In comparison to another state of the art approaches, we achieve an absolute mean 3.72% improvement in performance on the Visual Genome dataset.
A2D2: Audi Autonomous Driving Dataset

Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous Driving Dataset (A2D2). Our dataset consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentation, instance segmentation, and data extracted from the automotive bus. Our sensor suite consists of six cameras and five LiDAR units, providing full 360 degree coverage. The recorded data is time synchronized and mutually registered. Annotations are for non-sequential frames: 41,277 frames with semantic segmentation image and point cloud labels, of which 12,497 frames also have 3D bounding box annotations for objects within the field of view of the front camera. In addition, we provide 392,556 sequential frames of unannotated sensor data for recordings in three cities in the south of Germany. These sequences contain several loops. Faces and vehicle number plates are blurred due to GDPR legislation and to preserve anonymity. A2D2 is made available under the CC BY-ND 4.0 license, permitting commercial use subject to the terms of the license. Data and further information are available at http://www.a2d2. audi.
Star Formation Occurs in Dense Gas, but What Does “Dense” Mean?

We report results of a project to map HCN and emission toward a sample of molecular clouds in the inner Galaxy, all containing dense clumps that are actively engaged in star formation. We compare these two molecular line tracers with millimeter continuum emission and extinction, as inferred from 13CO, as tracers of dense gas in molecular clouds. The fraction of the line luminosity from each tracer that comes from the dense gas, as measured by mag, varies substantially from cloud to cloud. In all cases, a substantial fraction (in most cases, the majority) of the total luminosity arises in gas below the mag threshold and outside the region of strong millimeter continuum emission. Measurements of toward other galaxies will likely be dominated by such gas at lower surface densities. Substantial, even dominant, contributions to the total line luminosity can arise in gas with densities typical of the cloud as a whole (n ∼ 100 cm−3). Defining the dense clump from the HCN or emission itself, similarly to previous studies, leads to a wide range of clump properties, with some being considerably larger and less dense than in previous studies. HCN and have a similar ability to trace dense gas for the clouds in this sample. For the two clouds with low virial parameters, 13CO is definitely a worse tracer of the dense gas, but for the other four, it is equally good (or bad) at tracing dense gas.
Error analysis for filtered back projection reconstructions in Besov spaces

Filtered back projection (FBP) methods are the most widely used reconstruction algorithms in computerized tomography (CT). The ill-posedness of this inverse problem allows only an approximate reconstruction for given noisy data. Studying the resulting reconstruction error has been a most active field of research in the 1990s and has recently been revived in terms of optimal filter design and estimating the FBP approximation errors in general Sobolev spaces. However, the choice of Sobolev spaces is suboptimal for characterizing typical CT reconstructions. A widely used model are sums of characteristic functions, which are better modelled in terms of Besov spaces Bqα,p(R2) . In particular B1α,1(R2) with α ≈ 1 is a preferred model in image analysis for describing natural images. In case of noisy Radon data the total FBP reconstruction error ‖f−fLδ‖⩽‖f−fL‖+‖fL−fLδ‖ splits into an approximation error and a data error, where L serves as regularization parameter. In this paper, we study the approximation error of FBP reconstructions for target functions f∈L1(R2)∩Bqα,p(R2) with positive α∉N and 1 ⩽ p, q ⩽ ∞. We prove that the L p -norm of the inherent FBP approximation error f − f L can be bounded above by ‖f−fL‖Lp(R2)⩽cα,q,WL−α|f|Bqα,p(R2) under suitable assumptions on the utilized low-pass filter’s window function W. This then extends by classical methods to estimates for the total reconstruction error.
Predicting FIR lines from simulated galaxies

Far-infrared (FIR) emission lines are a powerful tool to investigate the properties of the interstellar medium, especially in high-redshift galaxies, where ALMA observations have provided unprecedented information. Interpreting such data with state-of-the-art cosmological simulations post-processed with CLOUDY, has provided insights on the internal structure and gas dynamics of these systems. However, no detailed investigation of the consistency and uncertainties of this kind of analysis has been performed to date. Here, we compare different approaches to estimate FIR line emission from state-of-the-art cosmological simulations, either with CLOUDY or with on-the-fly non-equilibrium chemistry. We find that [CII]$_{158\mu}$ predictions are robust. [OI] emission lines are instead model-dependent, as these lines are strongly affected by the thermodynamic state of the gas and non-equilibrium photoionisation effects. For the same reasons, [OI] lines represent an excellent ISM diagnostic. Future observations targeting these lines will be also crucial to constrain models.