Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach

The empirical studies of Graph Neural Networks (GNNs) broadly take the original node feature and adjacency relationship as singleview input, ignoring the rich information of multiple graph views. To circumvent this issue, the multiview graph analysis framework has been developed to fuse graph information across views. How to model and integrate shared (i.e. consistency) and view-specific (i.e. complementarity) information is a key issue in multiview graph analysis. In this paper, we propose a novel Multiview Variational Graph Information Bottleneck (MVGIB) principle to maximize the agreement for common representations and the disagreement for view-specific representations. Under this principle, we formulate the common and view-specific information bottleneck objectives across multiviews by using constraints from mutual information. However, these objectives are hard to directly optimize since the mutual information is computationally intractable. To tackle this challenge, we derive variational lower and upper bounds of mutual information terms, and then instead optimize variational bounds to find the approximate solutions for the information objectives. Extensive experiments on graph benchmark datasets demonstrate the superior effectiveness of the proposed method.
On Divergence Measures for Bayesian Pseudocoresets

A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesian pseudocoresets is not yet validated in realistic problems such as image classification with deep neural networks. On the other hand, dataset distillation methods similarly construct a small dataset such that the optimization using the synthetic dataset converges to a solution with performance competitive with optimization using full data. Although dataset distillation has been empirically verified in large-scale settings, the framework is restricted to point estimates, and their adaptation to Bayesian inference has not been explored. This paper casts two representative dataset distillation algorithms as approximations to methods for constructing pseudocoresets by minimizing specific divergence measures: reverse KL divergence and Wasserstein distance. Furthermore, we provide a unifying view of such divergence measures in Bayesian pseudocoreset construction. Finally, we propose a novel Bayesian pseudocoreset algorithm based on minimizing forward KL divergence. Our empirical results demonstrate that the pseudocoresets constructed from these methods reflect the true posterior even in high-dimensional Bayesian inference problems.
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics

We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. We combine these strict constraints with a hierarchical network architecture, a carefully constructed resampling scheme, and a training approach for temporal coherence. In combination, the proposed method allows us to increase the physical accuracy of the learned simulator substantially. In addition, the induced physical bias leads to significantly better generalization performance and makes our method more reliable in unseen test cases. We evaluate our method on a range of different, challenging fluid scenarios. Among others, we demonstrate that our approach generalizes to new scenarios with up to one million particles. Our results show that the proposed algorithm can learn complex dynamics while outperforming existing approaches in generalization and training performance. An implementation of our approach is available at https://github.com/tum-pbs/DMCF.
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based controller synthesis method for continuous-state models with stochastic noise and uncertain parameters. By sampling techniques and robust analysis, we capture both aleatoric and epistemic uncertainty, with a user-specified confidence level, in the transition probability intervals of a so-called interval Markov decision process (iMDP). We synthesize an optimal policy on this iMDP, which translates (with the specified confidence level) to a feedback controller for the continuous model with the same performance guarantees. Our experimental benchmarks confirm that accounting for epistemic uncertainty leads to controllers that are more robust against variations in parameter values.
Kinematics of geodesic flows in stringy black hole backgrounds

We study the kinematics of timelike geodesic congruences in two and four dimensions in spacetime geometries representing stringy black holes. The Raychaudhuri equations for the kinematical quantities (namely, expansion, shear, and rotation) characterizing such geodesic flows are written down and subsequently solved analytically (in two dimensions) and numerically (in four dimensions) for specific geodesic flows. We compare between geodesic flows in dual (electric and magnetic) stringy black hole backgrounds in four dimensions, by showing the differences that arise in the corresponding evolutions of the kinematic variables. The crucial role of initial conditions and the spacetime curvature on the evolution of the kinematical variables is illustrated. Some novel general conclusions on caustic formation and geodesic focusing are obtained from the analytical and numerical findings. We also propose a new quantifier in terms of the time (affine parameter) of approach to a singularity, which may be used to distinguish between flows in different geometries. In summary, our quantitative findings bring out hitherto unknown features of the kinematics of geodesic flows, which, otherwise, would have remained overlooked, if we confined ourselves to only a qualitative analysis.
Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation

Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base. Previous research showed that entity overshadowing is a significant challenge for existing ED models: when presented with an ambiguous entity mention, the models are much more likely to rank a more frequent yet less contextually relevant entity at the top. Here, we present NICE, an iterative approach that uses entity type information to leverage context and avoid over-relying on the frequency-based prior. Our experiments show that NICE achieves the best performance results on the overshadowed entities while still performing competitively on the frequent entities.
Mass estimates for very cold (< 8 K) gas in molecular cloud cores

The mass of prestellar cores is an essential ingredient to understand the onset of star formation in the core. The low level of emission from cold dust may keep parts of it hidden from observation. We aim to determine the fraction of core mass in the temperature range < 8 K that can be expected for typical low- and high-mass star formation regions. We calculate the dust temperature within standard spherically symmetric prestellar cores for a grid of density powerlaws in the outer core regions, core masses, and variation in the external multi-component radiation field. The dust is assumed to be composed of amorphous silicate and carbon, and variations of its optical properties are discussed. As measure for the distribution of cores and clumps, we use core mass functions derived for various environments. In view of the high densities in very cold central regions, dust and gas temperatures are assumed to be equal. We find that the fraction of mass with temperatures < 8 K in typical low- and high-mass cores is < 20\%. It is possible to obtain higher fractions of very cold gas by placing intermediate- or high-mass cores in a typical low-mass star formation environment. We show that the mass uncertainty arising from FIR to mm modeling of very cold dust emission is smaller than the mass uncertainty due to the unknown dust opacities.
A Proposal For Solving The Cosmological Constant Problem

Recently, we used a reinterpretation of the generalized uncertainty principle as an effective variation of the Planck constant (Phys. Lett. B 831 (2022) 137182). In this letter, we show that the effective Planck constant sets a consistency between the theoretical vacuum energy density computed in quantum field theory, and the observed vacuum energy density. This ameliorates the cosmological constant problem.
On the apparent absence of broad iron lines in Seyfert galaxies

We present an analysis of XMM–Newton observations of 11 Seyfert galaxies that appear to be missing a broad iron Kα line. These objects represent a challenge to the established paradigm for active galactic nuclei, where a relatively cold accretion disc feeds the central black hole. In that paradigm, X-ray illumination of the accretion disc should lead to continuum and fluorescence emission from iron which is broadened and shifted by relativistic effects close to the hole. We extend the work of Nandra et al., who found no evidence for such a component in an earlier analysis of these objects, by testing a variety of more complex relativistic reflection models. Specifically, we consider the possibility that the disc is highly ionized, and/or that the reflection is heavily blurred by strong relativistic effects in a Kerr geometry. We find that in 8/11 of the observations with no apparent broad iron line, the fit is significantly improved when an ionized or strongly blurred reflector is included, and that all 11 observations allow for such a component. The disc inclinations are found generally to be around 60 ◦ , which when combined with a steep emissivity profile results in strong relativistic blurring of the reflection, rendering the Kα line difficult to distinguish from the underlying continuum. Nevertheless, relativistic reflection does appear to be present, and the strength of the smeared reflection is similar to that expected from a flat disc illuminated by a point source. Such blurred reflection and the associated steep radial emissivity profiles are consistent with the gravitational light bending of the continuum photons close to the black hole.
ANALYSIS OF THE MOTION OF AN EXTRASOLAR PLANET IN A BINARY SYSTEM

More than 10% of extra-solar planets (EPs) orbit in a binary or multiple stellar system. We investigated the motion of planets revolving in binary systems in the case of the three-body problem. We carried out an analysis of the motion of an EP revolving in a binary system with the following conditions: (1) a planet in a binary system revolves around one of the components (parent star); (2) the distance between the star's components is greater than that between the parent star and the orbiting planet (ratio of the semi-major axes is a small parameter); and (3) the mass of the planet is smaller than the mass of the stars, but is not negligible. The Hamiltonian of the system without short periodic terms was used. We expanded the Hamiltonian in terms of the Legendre polynomial and truncated after the second-order term, depending on only one angular variable. In this case, the solution of the system was obtained and the qualitative analysis of the motion was produced. We have applied this theory to real EPs and compared to the numerical integration. Analyses of the possible regions of motion are presented. It is shown that stable and unstable motions of EPs are possible. We applied our calculations to two binary systems hosting an EP and calculated the possible values for their unknown orbital elements.
The HST survey of Magellanic-Cloud clusters and of their stellar populations

Abstract A large number of intermediate-age (~1-2-Gyr old) globular clusters (GCs) in the Large and the Small Magellanic Cloud (MC) exhibit either bimodal or extended main-sequence (MS) turn off and dual red clump (RC). Moreover, recent papers have shown that the MS of the young clusters NGC 1844 and NGC 1856 is either broadened or split. These features of the color-magnitude diagram (CMD) are not consistent with a single isochrone and suggest that star clusters in MCs have experienced a prolonged star formation, in close analogy with Milky-Way GCs with multiple stellar populations. As an alternative, stellar rotation or interacting binaries can be responsible of the CMD morphology. In the following I will summarize the observational scenario and provide constraints on the nature of the complex CMD of young and intermediate-age MC clusters from our ongoing photometric survey with the Hubble Space Telescope (HST).
Brief Summary of Existing Research on Students’ Conceptions of AI

Already now, pupils come into contact with many phenomena of computer science (CS), especially artificial intelligence (AI) technologies, which appear in different forms and in a variety of applications, in their everyday lives. In this work we present insights into students’ conceptions of AI. For this purpose already conducted research in the field is analysed and initial findings are presented. According to these, students associate AI particularly with autonomously acting systems, which are characterised primarily by human characteristics or behaviour. Finally avenues for further research are identified.
InviSeal: A Stealthy Dynamic Analysis Framework for Android Systems

With wide adaptation of open-source Android into mobile devices by different device vendors, sophisticated malware are developed to exploit security vulnerabilities. As comprehensive security analysis on physical devices are impractical and costly, emulator-driven security analysis has gained popularity in recent times. Existing dynamic analysis frameworks suffer from two major issues: (i) they do not provide foolproof anti-emulation-detection measures even for fingerprint-based attacks, and (ii) they lack efficient cross-layer profiling capabilities. In this work, we present InviSeal, a comprehensive and scalable dynamic analysis framework that includes low-overhead cross-layer profiling techniques and detailed anti-emulation-detection measures along with the basic emulation features. While providing an emulator-based comprehensive analysis platform, InviSeal strives to remain behind-the-scene to avoid emulation-detection. We empirically demonstrate that the proposed OS layer profiling utility to achieve cross-layer profiling is ∼1.26× faster than existing strace-based approaches. Overall, on average, InviSeal incurs ∼1.04× profiling overhead in terms of the number of operations performed by the various workloads of the CaffeineMark-3.0 benchmark, which is better than the contemporary techniques. Furthermore, we measure the anti-emulation-detection strategies of InviSeal against the fingerprint-based emulation-detection attacks. Experimental results show that the emulation-detection attacks carried out by the malware samples do not find InviSeal as an emulated platform.
Analysis of Software Engineering Skills Gap in the Industry

Many practitioners might struggle with becoming productive in different software engineering (SE) roles due to misalignment of the skills learnt during the university time with what is expected in the industry. Companies spend significant resources to train the personnel, whose academic backgrounds are not only based on “computing disciplines”. Hiring properly trained practitioners allows employers to spend less time while incorporating them more efficiently into the workforce; for employees, knowing the most important skillset is helpful to increase their chance of employability. On the other hand, for academia, understanding the necessary skillset is critical to make curriculum updates. To achieve these objectives, we conducted a survey, which was responded to by 628 software practitioners, who completed their undergraduate degree in Turkey, working in 13 countries. This paper sheds light on the most important (hard and soft) skills in the industry by presenting various cross-factor analyses as well as their coverage in the academic curriculum (mostly in Turkish universities). The results showed that the most important skills are related to various factors such as profiles of the practitioners (e.g., SE role(s), work experience) and the characteristics of the product developed by the practitioner. The findings revealed that both academia and industry should invest in skills improvement: academia can make necessary educational updates according to industrial needs; whereas industry can provide practical experiences to students. By creating the awareness of the expected skillset, both practitioners and academics will benefit from the results, which help close the gaps that can and should be achieved through more Industry Academia Collaborations (IACs).
A Model for Infusing Computational Thinking Skills at Teacher Colleges in a Developing Country

Current advances emphasize that young learners benefit from developing and applying computational thinking (CT) skills to solve problems in a variety of subjects, not limited to mathematics and science. Under this scenario, the inclusion of CT in primary and secondary education will not scale up if pre-service teachers do not actively acquire these skills during their formation. However, how this should be done is not clear. We propose an integration model to infuse CT skills at teacher colleges in Chile, which takes into account our previous work on the topic, as well as current standards, interests, and educational policies in Chile.
H1 Approach Control for Regulation of Active Car Suspension

A method and an apparatus for profile guided TLB's (translation look-aside buffer) and cache optimization in an operating system. A typical operating system has a working set of information for any application which is running at some time. This working set of information can be written out by the operating system in some section of the object file. Once this information is in the object file, it may be utilized by the operating system in various ways. The method and apparatus decreases TLB misses for the benchmarks, disables infrequently used pages from disturbing the caches, and provides better rates on caches. This and many other advantages of the invention allow an increased efficiency and optimization of a given operating system.
A Survey on Virtual Network Functions for Media Streaming: Solutions and Future Challenges

Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.
FIVE GROUPS OF RED GIANTS WITH DISTINCT CHEMICAL COMPOSITION IN THE GLOBULAR CLUSTER NGC 2808

The chemical composition of multiple populations in the massive globular cluster (GC) NGC 2808 is addressed with the homogeneous abundance reanalysis of 140 red giant branch stars. UVES spectra for 31 stars and GIRAFFE spectra for the other giants were analyzed with the same procedures used for about 2500 giants in 23 GCs in our FLAMES survey, deriving abundances of Fe, O, Na, Mg, Si, Ca, Ti, Sc, Cr, Mn, and Ni. Iron, elements from &agr; ? > capture, and those in the Fe group do not show intrinsic scatter. On our UVES scale, the metallicity of NGC 2808 is [Fe/H] = − 1.129 ± 0.005 ± 0.034 ? > (± statistical ± systematic error) with σ = 0.030 (31 stars). The main features related to proton-capture elements are retrieved, but the improved statistics and the smaller associated internal errors allow us to uncover five distinct groups of stars along the Na–O anticorrelation. We observe large depletions in Mg, anticorrelated with enhancements of Na and also Si, suggestive of unusually high temperatures for proton captures. About 14% of our sample is formed by giants with solar or subsolar [Mg/Fe] ratios. Using the [Na/Mg] ratios, we confirm the presence of five populations with different chemical compositions that we call P1, P2, I1, I2, and E in order of decreasing Mg and increasing Na abundances. Statistical tests show that the mean ratios in any pair of groups cannot be extracted from the same parent distribution. The overlap with the five populations recently detected from UV photometry is good but not perfect, confirming that more distinct components probably exist in this complex GC.
Multiple Temporal Pooling Mechanisms for Weakly Supervised Temporal Action Localization

Recent action localization works learn in a weakly supervised manner to avoid the expensive cost of human labeling. Those works are mostly based on the Multiple Instance Learning framework, where temporal pooling is an indispensable part that usually relies on the guidance of snippet-level Class Activation Sequences (CAS). However, we observe that previous works only leverage a simple convolutional neural network for the generation of CAS, which ignores the weak discriminative foreground action segments and the background ones, and meanwhile, the relationship between different actions has not been considered. To solve this problem, we propose multiple temporal pooling mechanisms (MTP) for a more sufficient information utilization. Specifically, with the design of the Foreground Variance Branch, Dual Foreground Attention Branch and Hybrid Attention Fine-tuning Branch, MTP can leverage more effective information from different aspects and generate different CASs to guide the learning of temporal pooling. Moreover, different loss functions are designed for a better optimization of individual branches, aiming to effectively distinguish the action from the background. Our method shows excellent results on the THUMOS14 and ActivityNet1.2 datasets.
Exploring Transformers models for Emotion Recognition: a comparision of BERT, DistilBERT, RoBERTa, XLNET and ELECTRA

This paper investigates the performance of different Transformers Models for Emotion Recognition, a specific NLU (Natural Language Understanding) task in Affective Computing. We implemented and finetuned different transformers language models (BERT, DistilBERT, RoBERTa, XLNet, and ELECTRA) using a fine-grained emotion dataset with 28 emotions classes Our initial hypothesis was that the model size would not significantly impact this specific task. We evaluated and compared all those models in terms of performance (f1-score) and time to complete. Except for the ELECTRA model, which had the worst F1-score (.33), the other models had more similar results. RoBERTa achieved the best F1-score (.49), followed by DistillBERT (.48), XLNet (.48), and then BERT (.46). However, when we look at the metric of computational cost and time to complete, we argue that the DistillBERT, which is a smaller model, is the most efficient for this type of task.