Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a new three‐dimensional mathematical ionosphere model at European Space Agency/European Space Operations Centre

For several years a clear trend from the application of classical so‐called “single layer” models to attempts to model the ionosphere in accordance to its real three‐dimensional nature can be observed (see, e.g., Scherliess et al., 2003; Hernández‐Pajares et al., 1999). European Space Agency (ESA)/European Space Operations Centre (ESOC) commenced in 1998 employing a three‐dimensional (3‐D) model for ionosphere processing (Feltens, 1998; Feltens et al., 1998). This first version of a 3‐D model at ESOC model was based on a simple Chapman profile approach, assuming that the vertical component of the ionosphere could be mathematically expressed in terms of a single β‐layer Chapman profile function. The profile function's parameters, maximum electron density N0 and its height h0, were in turn expressed as surface functions of geomagnetic latitude and local time whose coefficients were estimated. In this way a horizontal variation of N0 and h0 was modeled, and the profile function varied vertically, depending on the actual N0 and h0 values at a certain location. The ionosphere, however, consists of several layers. Additionally, the plasmasphere on top of the ionosphere must be accounted for, and the scale height, needed to compute the profile function z‐parameter, is height‐dependent. Furthermore, some of the ionosphere layers are so called α‐layers and some parts of the ionosphere show special behavior. All these effects must be accounted for in a proper 3‐D mathematical modeling. It is the intent of this paper to give a substantial overview over the 3‐D ionosphere models developed at ESOC and their current testing and implementation status.
Life in complex fluids: Swimming in polymers

Many microorganisms live and evolve in complex fluids. Examples include mammalian spermatozoa in cervical mucus, worms (e.g., \textit{C. elegans}) in wet soil, and bacteria (e.g., \textit{H. pylori}) in our stomach lining. Due to the presence of (bio)polymers and/or solids, such fluids often display nonlinear response to (shear) stresses including viscoelasticity and shear-rate dependent viscosity. The successful interaction between these microorganisms and their fluid environment is critical to the function of many biological processes including human reproduction, ecosystem dynamics, and the spread of disease \&infection. This interaction is often nonlinear and can lead to many unexpected behavior. Here, I will discuss developments in characterizing, modeling, and understanding the swimming behavior of model microorganism in viscoelastic and shear-thinning fluids. Three main microorganisms will be explored: (i) the nematode \textit{C. elegans}, an undulatory swimmer; (ii) the green algae \textit{C. reinhardtii}, a puller swimmer; and (iii) the bacterium \textit{E. coli}, a pusher swimmer. Investigation with artificial particles/swimmers will also be discussed; such studies are helpful in decoupling the biology from hydrodynamic effects. We will explore the interactions between these swimmers' gaits, geometry, and actuation and fluid rheological behavior using mostly experiments, and discuss these results relative to numerical and analytical predictions.
Review on long time asymptotics of large data in some nonintegrable dispersive models

In this short note, we review recent results concerning the long time dynamics of large data solutions to several dispersive models. Starting with the KdV case and ending with the KP models, we review the literature and present new results where virial estimates allow one to prove local energy decay in different regions of space where solitons, lumps or solitary waves are not present.
A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean

A conﬁdence sequence (CS) is an anytime-valid sequential inference primitive which produces an adapted sequence of sets for a predictable parameter sequence with a time-uniform coverage guarantee. This work constructs a non-parametric non-asymptotic lower CS for the running average conditional expectation whose slack converges to zero given non-negative right heavy-tailed observations with bounded mean. Speciﬁcally, when the variance is ﬁnite the approach dominates the empirical Bernstein supermartingale of Howard et al. [5]; with inﬁnite variance, can adapt to a known or unknown (1 + δ )-th moment bound; and can be eﬃciently approximated using a sublinear number of suﬃcient statistics. In certain cases this lower CS can be converted into a closed-interval CS whose width converges to zero, e.g., any bounded realization, or post contextual-bandit inference with bounded rewards and unbounded importance weights. A refer-ence implementation and example simulations demonstrate the technique.
Private Algorithms with Private Predictions

When applying diﬀerential privacy to sensitive data, a common way of getting improved performance is to use external information such as other sensitive data, public data, or human priors. We propose to use the algorithms with predictions framework—previously applied largely to improve time complexity or competitive ratios—as a powerful way of designing and analyzing privacy-preserving methods that can take advantage of such external information to improve utility . For four important tasks—quantile release, its extension to multiple quantiles, covariance estimation, and data release—we construct prediction-dependent diﬀerentially private methods whose utility scales with natural measures of prediction quality. The analyses enjoy several advantages, including minimal assumptions about the data, natural ways of adding robustness to noisy predictions, and novel “meta” algorithms that can learn predictions from other (potentially sensitive) data. Overall, our results demonstrate how to enable diﬀerentially private algorithms to make use of and learn noisy predictions, which holds great promise for improving utility while preserving privacy across a variety of tasks.
Large-batch Optimization for Dense Visual Predictions

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tackle this challenge. However, although the current advanced algorithms such as LARS and LAMB succeed in classification models, the complicated pipelines of dense visual predictions such as object detection and segmentation still suffer from the heavy performance drop in the large-batch training regime. To address this challenge, we propose a simple yet effective algorithm, named Adaptive Gradient Variance Modulator (AGVM), which can train dense visual predictors with very large batch size, enabling several benefits more appealing than prior arts. Firstly, AGVM can align the gradient variances between different modules in the dense visual predictors, such as backbone, feature pyramid network (FPN), detection, and segmentation heads. We show that training with a large batch size can fail with the gradient variances misaligned among them, which is a phenomenon primarily overlooked in previous work. Secondly, AGVM is a plug-and-play module that generalizes well to many different architectures (e.g., CNNs and Transformers) and different tasks (e.g., object detection, instance segmentation, semantic segmentation, and panoptic segmentation). It is also compatible with different optimizers (e.g., SGD and AdamW). Thirdly, a theoretical analysis of AGVM is provided. Extensive experiments on the COCO and ADE20K datasets demonstrate the superiority of AGVM. For example, it can train Faster R-CNN+ResNet50 in 4 minutes without losing performance. AGVM enables training an object detector with one billion parameters in just 3.5 hours, reducing the training time by 20.9x, whilst achieving 62.2 mAP on COCO. The deliverables are released at https://github.com/Sense-X/AGVM.
On Feature Learning in the Presence of Spurious Correlations

Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about the core (non-spurious) features that can be decoded from the representations learned by standard empirical risk minimization (ERM) and specialized group robustness training. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. On multiple vision and NLP problems, we show that the features learned by simple ERM are highly competitive with the features learned by specialized group robustness methods targeted at reducing the effect of spurious correlations. Moreover, we show that the quality of learned feature representations is greatly affected by the design decisions beyond the training method, such as the model architecture and pre-training strategy. On the other hand, we find that strong regularization is not necessary for learning high quality feature representations. Finally, using insights from our analysis, we significantly improve upon the best results reported in the literature on the popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems, achieving 97%, 92% and 50% worst-group accuracies, respectively.
A search for thermal gyro-synchrotron emission from hot stellar coronae

We searched for thermal gyro-synchrotron radio emission from a sample of five radio-loud stars whose X-ray coronae contain a hot (Te > 107 K) thermal component. We used the JVLA to measure Stokes I and V/I spectral energy distributions (SEDs) over the frequency range 15–45 GHz, determining the best-fitting model parameters using power-law and thermal gyro-synchrotron emission models. The SEDs of the three chromospherically active binaries (Algol, UX Arietis, HR 1099) were well-fit by a power-law gyro-synchrotron model, with no evidence for a thermal component. However, the SEDs of the two weak-lined T Tauri stars (V410 Tau, HD 283572) had a circularly polarized enhancement above 30 GHz that was inconsistent with a pure power-law distribution. These spectra were well-fit by summing the emission from an extended coronal volume of power-law gyro-synchrotron emission and a smaller region with thermal plasma and a much stronger magnetic field emitting thermal gyro-synchrotron radiation. We used Bayesian inference to estimate the physical plasma parameters of the emission regions (characteristic size, electron density, temperature, power-law index, and magnetic field strength and direction) using independently measured radio sizes, X-ray luminosities, and magnetic field strengths as priors, where available. The derived parameters were well-constrained but somewhat degenerate. The power-law and thermal volumes in the pre-main-sequence stars are probably not co-spatial, and we speculate they may arise from two distinct regions: a tangled-field magnetosphere where reconnection occurs and a recently discovered low-latitude poloidal magnetic field, respectively.
The State-of-the-Art in AI-Based Malware Detection Techniques: A Review

Artificial Intelligence techniques have evolved rapidly in recent years, revolutionising the approaches used to fight against cybercriminals. But as the cyber security field has progressed, so has malware development, making it an economic imperative to strengthen businesses’ defensive capability against malware attacks. This review aims to outline the state-of-the-art AI techniques used in malware detection and prevention, providing an in-depth analysis of the latest studies in this field. The algorithms investigated consist of Shallow Learning, Deep Learning and Bio-Inspired Computing, applied to a variety of platforms, such as PC, cloud, Android and IoT. This survey also touches on the rapid adoption of AI by cybercriminals as a means to create ever more advanced malware and exploit the AI algorithms designed to defend against them.
3D Human Mesh Construction Leveraging Wi-Fi

In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system. Our system leverages the advances of WiFi to visualize the shape and deformations of the human body for 3D mesh construction. In particular, it leverages multiple transmitting and receiving antennas on WiFi devices to estimate two-dimensional angle of arrival (2D AoA) of the WiFi signal reflections to enable WiFi devices to “see” the physical environment as we humans do. It then extracts only the images of the human body from the physical environment, and leverages deep learning models to digitize the extracted human body into 3D mesh representation. Experimental evaluation under various indoor environments shows that Wi-Mesh achieves an average vertices location error of 2.81cm and joint position error of 2.4cm, which is comparable to the systems that utilize specialized and dedicated hardware. The proposed system has the advantage of re-using the WiFi devices that already exist in the environment for potential mass adoption. It can also work in non-line of sight (NLoS), poor lighting conditions, and baggy clothes, where the camera-based systems do not work well.
Relative inner amenability

Let $G$ be a subgroup of a discrete (countable) group $\Gamma$. We introduce a notion of relative inner amenability of $G$ in $\Gamma$, we prove some equivalent conditions and provide examples as well as counter-examples. We also discuss briefly the corresponding relative property gamma for pairs of finite factors and we deduce from this a characterization of discrete, icc groups which do not have Kazhdan's property $T$.
A multiscale method for inhomogeneous elastic problems with high contrast coefficients

In this paper, we develop the constrained energy minimizing generalized multiscale finite element method (CEM-GMsFEM) with mixed boundary conditions (Dirichlet and Neumann) for the elasticity equations in high contrast media. By a special treatment of mixed boundary conditions separately, and combining the construction of the relaxed and constraint version of the CEM-GMsFEM, we discover that the method offers some advantages such as the independence of the target region's contrast from precision, while the sizes of oversampling domains have a significant impact on numerical accuracy. Moreover, to our best knowledge, this is the first proof of the convergence of the CEM-GMsFEM with mixed boundary conditions for the elasticity equations given. Some numerical experiments are provided to demonstrate the method's performance.
Very Metal-poor Stars in the Solar Vicinity: Age Determination

The ages of the oldest and most metal-poor stars in the Milky Way bear important information on the age of the universe and its standard model. We analyze a sample of 28 extremely metal-poor field stars in the solar vicinity culled from the literature and carefully determine their ages. To this aim, we critically make use of Gaia data to derive their distances and associated uncertainties. Particular attention has been paid to the estimate of the reddening and its effect on the derivation of stellar ages. We employed different reddenings and superimpose isochrones from different sources on the stars' color–magnitude diagram built up with different photometric systems. We highlight subtle metallicity effects when using the Johnson photometry for low-metallicity stars and finally adopt Gaia photometry. An automatic fitting method is devised to assign ages to each individual star taking into account the uncertainties in the input parameters. The mean age of the sample turns out to be 13.9 ± 0.5 Gyr using Padova isochrones, and 13.7 ± 0.4 Gyr using BASTI isochrones. We found also a group of very metal-poor stars ([Fe/H] = −2.7 to −2.0 dex) with relatively young ages, in the range 8–10 Gyr.
Deep conditional transformation models for survival analysis

An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classiﬁers. Parametric, distribution-free approaches which make full use of survival time and censoring status have not received much attention. We present deep conditional transformation models (DCTMs) for survival outcomes as a unifying approach to parametric and semiparametric survival analysis. DCTMs allow the speciﬁcation of non-linear and non-proportional hazards for both tabular and non-tabular data and extend to all types of censoring and truncation. On real and semi-synthetic data, we show that DCTMs compete with state-of-the-art DL approaches to survival analysis.
Mapping the weak field limit of scalar-Gauss-Bonnet gravity

We derive the weak field limit of scalar-Gauss-Bonnet theory and place novel bounds on the parameter space using terrestrial and space-based experiments. In order to analyze the theory in the context of a wide range of experiments, we compute the deviations from Einstein gravity around source masses with planar, cylindrical, and spherical symmetry. We find a correction to the Newtonian potential around spherical and cylindrical sources that can be larger than PPN corrections sufficiently close to the source. We use this to improve on laboratory constraints on the scalar-Gauss-Bonnet coupling parameter $\Lambda$ by two orders of magnitude. Present laboratory and Solar System bounds reported here are superseded by tests deriving from black holes.
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts.
How Hate Speech Varies by Target Identity: A Computational Analysis

This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification
Reference Governor for Input-Constrained MPC to Enforce State Constraints at Lower Computational Cost

In this paper, a control scheme is developed based on an input constrained Model Predictive Control (MPC) law and the idea, usual of Reference Governors (RG), of modifying the reference command to enforce constraints. The proposed scheme, referred to as the RGMPC, can handle (possibly nonlinear) state and input constraints and only requires optimization for MPC with polytopic input constraints for which fast algorithms exist. Conditions are given that ensure recursive feasibility of the RGMPC scheme and finite-time convergence of the modified reference command to the desired reference command. Simulation results for a spacecraft rendezvous maneuver with linear and nonlinear constraints demonstrate that the RGMPC scheme has lower average computational time than state and input constrained MPC with similar performance.
A note on diffusion limits for stochastic gradient descent

In the machine learning literature stochastic gradient descent has recently been widely discussed for its purported implicit regularization properties. Much of the theory, that attempts to clarify the role of noise in stochastic gradient algorithms, has widely approximated stochastic gradient descent by a stochastic differential equation with Gaussian noise. We provide a novel rigorous theoretical justification for this practice that showcases how the Gaussianity of the noise arises naturally.
Meanders, hyperelliptic pillowcase covers, and the Johnson filtration

A BSTRACT . We provide minimal constructions of meanders with particular com- binatorics. Using these meanders, we give minimal constructions of hyperelliptic pillowcase covers with a single horizontal cylinder and simultaneously a single vertical cylinder so that one or both of the core curves are separating curves on the underlying surface. In the case where both of the core curves are separating, we use these surfaces in a construction of Aougab-Taylor in order to prove that for any hyperelliptic connected component of the moduli space of quadratic differen- tials with no poles there exist ratio-optimising pseudo-Anosovs lying arbitrarily deep in the Johnson ﬁltration and stabilising the Teichm¨uller disk of a quadratic differential lying in this connected component.