Miracle-dz/UniFAR-FTUs huggingface dataset:
Some bounds on the size of maximum G-free sets in graphs

For given graph $H$, the independence number $\alpha(H)$ of $H$, is the size of the maximum independent set of $V(H)$. Finding the maximum independent set in a graph is a NP-hard problem. Another version of the independence number is defined as the size of the maximum induced forest of $H$, and called the forest number of $H$, and denoted by $f(H)$. Finding $f(H)$ is also a NP-hard problem. Suppose that $H=(V(H),E(H))$ be a graph, and $\G$ be a family of graphs, a graph $H$ has a $\G$-free $k$-coloring if there exists a decomposition of $V(H)$ into sets $V_i$, $i-1,2,\ldots,k$, so that $G\nsubseteq H[V_i]$ for each $i$, and $G\in\G$. $S\subseteq V(H)$ is $G$-free, where the subgraph of $H$ induced by $S$, be $G$-free, i.e. it contains no copy of $G$. Finding a maximum subset of $H$, so that $H[S]$ be a $G$-free graph is a very hard problem as well. In this paper, we study the generalized version of the independence number of a graph. Also giving some bounds about the size of the maximum $G$-free subset of graphs is another purpose of this article.
High-throughput determination of Hubbard 
U
 and Hund 
J
 values for transition metal oxides via the linear response formalism

DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J) calculation hinges on the accurate determination of its Hubbard U and Hund's J parameters, and the linear response (LR) methodology has proven to be computationally effective and accurate for calculating these parameters. This study provides a high-throughput computational analysis of the U and J values for transition metal d-electron states in a representative set of over 2000 magnetic transition metal oxides (TMOs), providing a frame of reference for researchers who use DFT+U to study transition metal oxides. In order to perform this high-throughput study, an atomate workflow is developed for calculating U and J values automatically on massively parallel supercomputing architectures. To demonstrate an application of this workflow, the spin-canting magnetic structure and unit cell parameters of the multiferroic olivine LiNiPO4 are calculated using the computed Hubbard U and Hund J values for Ni-d and O-p states, and are compared with experiment. Both the Ni-d U and J corrections have a strong effect on the Ni-moment canting angle. Additionally, including a O-p U value results in a significantly improved agreement between the computed lattice parameters and experiment.
The importance of silicate vapor in determining the structure, radii, and envelope mass fractions of sub-Neptunes

Substantial silicate vapor is expected to be in chemical equilibrium at temperature conditions typical of the silicate-atmosphere interface of sub-Neptune planets, which can exceed 5000 K. Previous models of the atmospheric structure and evolution of these exoplanets, which have been used to constrain their atmospheric mass fractions, have neglected this compositional coupling. In this work, we show that silicate vapor in a hydrogen-dominated atmosphere acts as a condensable species, decreasing in abundance with altitude. The resultant mean molecular weight gradient inhibits convection at temperatures above ∼4000 K, inducing a near-surface radiative layer. This radiative layer decreases the planet’s total radius compared to a planet with the same base temperature and a convective, pure H/He atmosphere. Therefore, we expect silicate vapor to have major effects on the inferred envelope mass fraction and thermal evolution of sub-Neptune planets. We demonstrate that differences in radii, and hence in inferred atmospheric masses, are largest for planets which have larger masses, equilibrium temperatures, and atmospheric mass fractions. The effects are largest for younger planets, but differences can persist on gigayear time-scales for some sub-Neptunes. For a 10M⊕ planet with Teq = 1000 K and an age of ∼300 Myr, an observed radius consistent with an atmospheric mass fraction of 10 per cent when accounting for silicate vapor would be misinterpreted as indicating an atmospheric mass fraction of 2 per cent if a H/He-only atmosphere were assumed. The presence of silicate vapor in the atmosphere is also expected to have important implications for the accretion and loss of primordial hydrogen atmospheres.
Get Your Foes Fooled: Proximal Gradient Split Learning for Defense Against Model Inversion Attacks on IoMT Data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 17.9<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, and 36.9<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> gains in accuracy over reconstructed and adversarial attacked images, respectively.
Ridge Polariton Laser: Different from a Semiconductor Edge-Emitting Laser

We experimentally demonstrate the difference between a ridge polariton laser, and a conventional edge-emitting ridge laser operating under electron-hole population inversion. The horizontal laser cavities are 20 -- 60 $\mu$m long GaN etched ridge structures with vertical Bragg reflectors. We investigate the laser threshold under optical pumping and assess quantitatively the effect of a varying optically-pumped length. The laser effect is achieved for an exciton reservoir length of just 15% of the cavity length, which would not be possible in a conventional ridge laser, with an inversion-less polaritonic gain about 10 times larger than in equivalent GaN lasers. The modelling of the cavity free spectral range demonstrates the polaritonic nature of the modes.
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?

Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly h elp p atients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time series that may be represented as switching between certain meta-states. To better predict impending seizures, retraining of prediction algorithms is therefore necessary and the retraining schedule should be adjusted to the change in meta-states. There is evidence that the nature of seizure-free interictal clips also changes with the transition between meta-states, which has been shown relevant for seizure prediction.
Beyond the Visible: A Survey on Cross-spectral Face Recognition

Cross-spectral face recognition (CFR) refers to recognizing individuals using face images stemming from different spectral bands, such as infrared vs. visible. While CFR is inherently more challenging than classical face recognition due to significant variation in facial appearance caused by the modality gap, it is useful in many scenarios including night-vision biometrics and detecting presentation attacks. Recent advances in convolutional neural networks (CNNs) have resulted in significant improvement in the performance of CFR systems. Given these developments, the contributions of this survey are three-fold. First, we provide an overview of CFR, by formalizing the CFR problem and presenting related applications. Secondly, we discuss the appropriate spectral bands for face recognition and discuss recent CFR methods, placing emphasis on deep neural networks. In particular we describe techniques that have been proposed to extract and compare heterogeneous features emerging from different spectral bands. We also discuss the datasets that have been used for evaluating CFR methods. Finally, we discuss the challenges and future lines of research on this topic.
The Ellipsoidal Universe and the Hubble tension

The Hubble tension resides in a statistically significant discrepancy between early time and late time determinations of the Hubble constant. We discuss the Hubble tension within the Ellipsoidal Universe cosmological model. We suggest that allowing small anisotropies in the large-scale spatial geometry could alleviate the tension. We, also, show that the discrepancy in the measurements of the Hubble constant is reduced to a statistically acceptable level if we assume sizeable cosmological anisotropies during the Dark Age. In addition, we argue that the Ellipsoidal Universe cosmological model should resolve the $S_8$ tension.
Exact learning and test theory

In this paper, based on results of exact learning and test theory, we study arbitrary infinite binary information systems each of which consists of an infinite set of elements and an infinite set of two-valued functions (attributes) defined on the set of elements. We consider the notion of a problem over information system, which is described by a finite number of attributes: for a given element, we should recognize values of these attributes. As algorithms for problem solving, we consider decision trees of two types: (i) using only proper hypotheses (an analog of proper equivalence queries from exact learning), and (ii) using both attributes and proper hypotheses. As time complexity, we study the depth of decision trees. In the worst case, with the growth of the number of attributes in the problem description, the minimum depth of decision trees of both types either is bounded from above by a constant or grows as a logarithm, or linearly. Based on these results and results obtained earlier for attributes and arbitrary hypotheses, we divide the set of all infinite binary information systems into seven complexity classes.
A Feature Extraction Based Model for Hate Speech Identification

The detection of hate speech online has become an important task, as offensive language such as hurtful, obscene and insulting content can harm marginalized people or groups. This paper presents TU Berlin team experiments and results on the task 1A and 1B of the shared task on hate speech and offensive content identification in Indo-European languages 2021. The success of different Natural Language Processing models is evaluated for the respective subtasks throughout the competition. We tested different models based on recurrent neural networks in word and character levels and transfer learning approaches based on Bert on the provided dataset by the competition. Among the tested models that have been used for the experiments, the transfer learning-based models achieved the best results in both subtasks.
Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Set

Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project's size and structure influence team productivity, a question famously considered in Brooks' law. Recent studies using massive repository data suggest that developers in larger teams tend to be less productive than smaller teams. Despite using similar methods and data, other studies argue for a positive linear or even super-linear relationship between team size and productivity, thus contesting the view of software economics that software projects are diseconomies of scale. In our work, we study challenges that can explain the disagreement between recent studies of developer productivity in massive repository data. We further provide, to the best of our knowledge, the largest, curated corpus of GitHub projects tailored to investigate the influence of team size and collaboration patterns on individual and collective productivity. Our work contributes to the ongoing discussion on the choice of productivity metrics in the operationalisation of hypotheses about determinants of successful software projects. It further highlights general pitfalls in big data analysis and shows that the use of bigger data sets does not automatically lead to more reliable insights.
TWO-ARMED GAUSSIAN BANDITS WITH UNKNOWN VARIANCES

We consider the fixed-budget best arm identification problem in two-armed Gaussian bandits with unknown variances. The tightest lower bound on the complexity and an algorithm whose performance guarantee matches the lower bound have long been open problems when the variances are unknown and when the algorithm is agnostic to the optimal proportion of the arm draws. In this paper, we propose a strategy comprising a sampling rule with randomized sampling (RS) following the estimated target allocation probabilities of arm draws and a recommendation rule using the augmented inverse probability weighting (AIPW) estimator, which is often used in the causal inference literature. We refer to our strategy as the RS-AIPW strategy. In the theoretical analysis, we first derive a large deviation principle for martingales, which can be used when the second moment converges in mean, and apply it to our proposed strategy. Then, we show that the proposed strategy is asymptotically optimal in the sense that the probability of misidentification achieves the lower bound by Kaufmann et al. (2016) when the sample size becomes infinitely large and the gap between the two arms goes to zero.
The Virtual Element Method for the 3D Resistive Magnetohydrodynamic model

We present a four-field Virtual Element discretization for the time-dependent resistive Magnetohydrodynamics equations in three space dimensions, focusing on the semi-discrete formulation. The proposed method employs general polyhedral meshes and guarantees velocity and magnetic fields that are divergence free up to machine precision. We provide a full convergence analysis under suitable regularity assumptions, which is validated by some numerical tests.
Understanding how people consume low quality and extreme news using web traffic data

To mitigate the spread of fake news, researchers need to understand who visit fake new sites, what brings people to those sites, where visitors come from, and what content they prefer to consume. In this paper, we analyze web traffic data from The Gateway Pundit (TGP), a popular far-right website that is known for repeatedly sharing false information that has made its web traffic available to the general public. We collect data on 68 million web traffic visits to the site over a month period and analyze how people consume news via multiple features. Our traffic analysis shows that search engines and social media platforms are main drivers of traffic; our geo-location analysis reveals that TGP is more popular in counties that voted for Trump in 2020; and our topic analysis shows that conspiratorial articles receive more visits than factual articles. Due to the inability to observe direct website traffic, existing research uses alternative data source such as engagement signals from social media posts. To validate if social media engagement signals correlate with actual web visit counts, we collect all Facebook and Twitter posts with URLs from TGP during the same time period. We show that all engagement signals positively correlate with web visit counts, but with varying correlation strengths. Metrics based on Facebook posts correlate better than metrics based on Twitter. Our unique web traffic data set and insights can help researchers to better measure the impact of far-right and fake news URLs on social media platforms.
Asymmetric modulation of Majorana excitation spectra and nonreciprocal thermal transport in the Kitaev spin liquid under a staggered magnetic field

We present the theoretical results for the Majorana band structure and thermal transport properties in the Kitaev honeycomb model under a staggered magnetic field in addition to a uniform one. The Majorana band structure is asymmetrically modulated in momentum space, and the valley degree of freedom is activated and controlled by the magnitudes and directions of the uniform and staggered magnetic fields; as a result, the Majorana excitation is either gapped or gapless, the latter of which in general has Majorana Fermi surfaces, in contrast to the point nodes in the absence of the fields. We show that the asymmetric deformation of the Majorana band induces a nonlinear (nonreciprocal) thermal transport, in addition to the linear one. The field dependence of the linear thermal current is correlated with the magnitude of the Majorana gap, while that of the nonlinear thermal current is associated with the asymmetry of the Majorana excitation spectra. We show the estimates of the thermal currents and discuss that the linear response is experimentally measurable, whereas future improvement is required for the observation of the nonlinear one. We also discuss how the thermal currents are modulated by other additional effects of the magnetic field and contributions from conventional magnons, latter of which are expected in heterostructures to realize an internal staggered magnetic field.
A systematic review and meta-analysis uncovering the relationship between alcohol consumption and sickness absence. When type of design, data, and sickness absence make a difference

Aim Earlier research has revealed a strong relationship between alcohol use and sickness absence. The aim of this review was to explore and uncover this relationship by looking at differences in type of design (cross-sectional vs. longitudinal), type of data (self-reported vs. registered data), and type of sickness absence (long-term vs. short term). Method Six databases were searched through June 2020. Observational and experimental studies from 1980 to 2020, in English or Scandinavian languages reporting the results of the association between alcohol consumption and sickness absence among working population were included. Quality assessment, and statistical analysis focusing on differences in the likelihood of sickness absence on subgroup levels were performed on each association, not on each study. Differences in the likelihood of sickness absence were analyzed by means of meta-analysis. PROSPERO registration number: CRD42018112078. Results Fifty-nine studies (58% longitudinal) including 439,209 employees (min. 43, max. 77,746) from 15 countries were included. Most associations indicating positive and statistically significant results were based on longitudinal data (70%) and confirmed the strong/causal relationship between alcohol use and sickness absence. The meta-analysis included eight studies (ten samples). The increased risk for sickness absence was likely to be found in cross-sectional studies (OR: 8.28, 95% CI: 6.33–10.81), studies using self-reported absence data (OR: 5.16, 95% CI: 3.16–8.45), and those reporting short-term sickness absence (OR: 4.84, 95% CI: 2.73–8.60). Conclusion This review supports, but also challenges earlier evidence on the association between alcohol use and sickness absence. Certain types of design, data, and types of sickness absence may produce large effects. Hence, to investigate the actual association between alcohol and sickness absence, research should produce and review longitudinal designed studies using registry data and do subgroup analyses that cover and explain variability of this association.
Very large SiPM arrays with aggregated output

In this work we will document the design and the performances of a SiPM-based photo-detector with a surface area of 100 cm2 conceived to operate as a replacement for PMTs. The signals from 94 SiPMs are summed up to produce an aggregated output that exhibits in liquid nitrogen a dark count rate (DCR) lower than 100 cps over the entire surface, a signal to noise ratio better than 13, and a timing resolution better than 5.5 ns. The module feeds about 360 mW at 5 V with a dynamic range in excess of 500 photo-electrons on a 100 Ω differential line. The unit can also operate at room temperature, at the cost of an increase of DCR to 108 cps.
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small generated CNN model on a specific task is encoded by a high-capacity Transformer model, we effectively decouple the complexity of the large task space from the complexity of individual tasks. Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal and better performance is attained when the information about the task can modulate all model parameters. For larger models we discover that generating the last layer alone allows us to produce competitive or better results than those obtained with state-of-the-art methods while being end-to-end differentiable.
Spatial Dissipative Solitons in Graphene-Based Active Random Metamaterials

We investigate dissipative nonlinear dynamics in graphene-based active metamaterials composed of randomly dispersed graphene nano-flakes embedded within an externally pumped gain medium. We observe that graphene saturable nonlinearity produces a sub-critical bifurcation of nonlinear modes, enabling self-organization of the emitted radiation into several dissipative soliton structures with distinct topological charges. We systematically investigate the existence domains of such nonlinear waves and their spatio-temporal dynamics, finding that soliton vortices are unstable, thus enabling self-organization into single dissipative structures with vanishing topological charge, independently of the shape of the graphene nano-flakes. Our results shed light on self-organization of coherent radiation structures in disordered systems and are relevant for future cavity-free lasers and amplifier designs.
The Geometry of Navigation Problems

While many works exploiting an existing Lie group structure have been proposed for state estimation, in particular the invariant extended Kalman filter (IEKF), few papers address the construction of a group structure that allows casting a given system into the framework of invariant filtering. In this article, we introduce a large class of systems encompassing most problems involving a navigating vehicle encountered in practice. For those systems we introduce a novel methodology that systematically provides a group structure for the state space, including vectors of the body frame such as biases. We use it to derive observers having properties akin to those of linear observers or filters. The proposed unifying and versatile framework encompasses all systems, where IEKF has proved successful, improves state-of-the art “imperfect” IEKF for inertial navigation with sensor biases, and allows addressing novel examples, like GNSS antenna lever arm estimation.