Miracle-dz/UniFAR-FTUs huggingface dataset:
Exoplanets with ELT-METIS

Direct imaging is a powerful exoplanet discovery technique that is complementary to other techniques and offers great promise in the era of 30 m class telescopes. Space-based transit surveys have revolutionized our understanding of the frequency of planets at small orbital radii around Sun-like stars. The next generation of extremely large ground-based telescopes will have the angular resolution and sensitivity to directly image planets with R < 4 R⊕ around the very nearest stars. Here, we predict yields from a direct imaging survey of a volume-limited sample of Sun-like stars with the Mid-Infrared ELT Imager and Spectrograph (METIS) instrument, planned for the 39 m European Southern Observatory Extremely Large Telescope (ELT) that is expected to be operational towards the end of the decade. Using Kepler occurrence rates, a sample of stars with spectral types A-K within 6.5 pc, and simulated contrast curves based on an advanced model of what is achievable from coronagraphic imaging with adaptive optics, we estimated the expected yield from METIS using Monte Carlo simulations. We find the METIS expected yield of planets in the N2 band (10.10−12.40 μm) is 1.14 planets, which is greater than comparable observations in the L (3.70−3.95 μm) and M (4.70−4.90 μm) bands. We also determined a 24.6% chance of detecting at least one Jovian planet in the background limited regime assuming a 1 h integration. We calculated the yield per star and estimate optimal observing revisit times to increase the yield. We also analyzed a northern hemisphere version of this survey and found there are additional targets worth considering. In conclusion, we present an observing strategy aimed to maximize the possible yield for limited telescope time, resulting in 1.48 expected planets in the N2 band.
Thermophysical Investigation of Asteroid Surfaces. II. Factors Influencing Grain Size

Asteroid surfaces are subjected to mechanical weathering processes that result in the development and evolution of regolith. Two proposed mechanisms—impact bombardment and thermal fatigue—have been proposed as viable and dominant weathering processes. Previously, we compiled and estimated thermal inertias of several hundred asteroids (mostly in the main belt) for which we determined dependencies on temperature, diameter, and rotation period. In this work, we estimate grain sizes of asteroid regoliths from this large thermal inertia data set using thermal conductivity models. Following our previous work, we perform multivariate linear model fits to the grain size data set and quantify its dependency on diameter and rotation period. We find that the preferred model indicates that asteroid grain sizes are inversely dependent on object size for <10 km asteroids and exhibit no relationship above this size cutoff. Rotation period and grain size show a positive relationship when the rotation period is greater than ∼5 hr and an inverse relationship below this rotation period. These results indicate that both impact weathering and thermal fatigue are relevant regolith evolution mechanisms. We run post-hoc t-tests between spectral groups to infer the influence of composition on regolith grain sizes. We find that M-type (including suspected metal-rich objects) and E-type asteroids have larger grain sizes relative to our population sample and that P-type asteroids have distinctly smaller grains than other groups.
The Anarchy-Stability Tradeoff in Congestion Games

This work focuses on the design of incentive mechanisms in congestion games, a commonly studied model for competitive resource sharing. While the majority of the existing literature on this topic focuses on unilaterally optimizing the worst case performance (i.e., price of anarchy), in this manuscript we investigate whether optimizing for the worst case has consequences on the best case performance (i
AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training

Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this problem. Some of these methods achieved considerable performance by associating the flat local minima with forgetting mitigation in continual learning. However, they inevitably need (1) tedious hyperparameters tuning, and (2) additional computational cost. To alleviate these problems, in this paper, we propose a simple yet effective optimization method, called AlterSGD, to search for a flat minima in the loss landscape. In AlterSGD, we conduct gradient descent and ascent alternatively when the network tends to converge at each session of learning new knowledge. Moreover, we theoretically prove that such a strategy can encourage the optimization to converge to a flat minima. We verify AlterSGD on continual learning benchmark for semantic segmentation and the empirical results show that we can significantly mitigate the forgetting and outperform the state-of-the-art methods with a large margin under challenging continual learning protocols.
Diverging Quantum Speed Limits: A Herald of Classicality

This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator. S.C. gratefully acknowledges the Science Foundation Ireland Starting Investigator Research Grant “SpeedDemon” (No. 18/SIRG/5508) for financial support. S.D. acknowledges support from the U.S. National Science Foundation under Grant No. DMR2010127.
HDMapNet: An Online HD Map Construction and Evaluation Framework

Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which dynamically constructs the local semantics based on onboard sensor observations. Meanwhile, we introduce a semantic map learning method, dubbed HDMapNet. HDMapNet encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements in the bird's-eye view. We benchmark HDMapNet on nuScenes dataset and show that in all settings, it performs better than baseline methods. Of note, our camera-LiDAR fusion-based HDMapNet outperforms existing methods by more than 50 % in all metrics. In addition, we develop semantic-level and instance-level metrics to evaluate the map learning performance. Finally, we showcase our method is capable of predicting a locally consistent map. By introducing the method and metrics, we invite the community to study this novel map learning problem.
Homotopy types of $\mathrm{Spin}^c(n)$-gauge groups over $S^4$

e gauge group of a principal -bundle % over a space is the group of -equivariant homeomorphisms of % that cover the identity on. We consider the gauge groups of bundles over (4 with Spin2 (=), the complex spin group, as structure group and show how the study of their homotopy types reduces to that of Spin(=)-gauge groups over (4. We then advance on what is known by providing a partial classification for Spin(7)and Spin(8)-gauge groups over (4.
Continuous frames in tensor product Hilbert spaces, localization operators and density operators

Continuous frames and tensor products are important topics in theoretical physics. This paper combines those concepts. We derive fundamental properties of continuous frames for tensor product of Hilbert spaces. This includes, for example, the consistency property, i.e. preservation of the frame property under the tensor product, and the description of the canonical dual tensors by those on the Hilbert space level. We show the full characterization of all dual systems for a given continuous frame, a result interesting by itself, and apply this to dual tensor frames. Furthermore, we discuss the existence on non-simple tensor product (dual) frames. Continuous frame multipliers and their Schatten class properties are considered in the context of tensor products. In particular, we give sufficient conditions for obtaining partial trace multipliers of the same form, which is illustrated with examples related to short-time Fourier transform and wavelet localization operators. As an application, we offer an interpretation of a class of tensor product continuous frame multipliers as density operators for bipartite quantum states, and show how their structure can be restricted to the corresponding partial traces.
Hybrid no-signaling-quantum correlations

Fundamental investigations in non-locality have shown that while the no-signaling principle alone is not sufficient to single out the set of quantum non-local correlations, local quantum mechanics and no-signaling together exactly reproduce the set of quantum correlations in the two-party Bell scenario. Here, we introduce and study an intermediate hybrid no-signaling quantum set of non-local correlations that we term HNSQ in the multi-party Bell scenario where some subsystems are locally quantum while the remaining subsystems are only constrained by the no-signaling principle. Specifically, the set HNSQ is a super-quantum set of correlations derived from no-signaling assemblages by performing quantum measurements on the trusted subsystems. We show that in contrast to the set NS of no-signaling behaviors, there exist extreme points of HNSQ in the tripartite Bell scenario that admit quantum realization. As a tool for optimization over the set HNSQ, we introduce an outer hierarchy of semi-definite programming approximations to the set following an approach put forward by Doherty–Parrilo–Spedalieri. We perform an extensive numerical analysis of the maximal violation of the facet Bell inequalities in the three-party binary input–output scenario and study the corresponding self-testing properties. In contrast to the usual no-signaling correlations, the new set allows for simple security proofs of (one-sided)-device-independent applications against super-quantum adversaries.
Fast Solution of Fully Implicit Runge-Kutta and Discontinuous Galerkin in Time for Numerical PDEs, Part II: Nonlinearities and DAEs

Fully implicit Runge-Kutta (IRK) methods have many desirable accuracy and stability properties as time integration schemes, but high-order IRK methods are not commonly used in practice with large-scale numerical PDEs because of the difficulty of solving the stage equations. This paper introduces a theoretical and algorithmic framework for solving the nonlinear equations that arise from IRK methods (and discontinuous Galerkin discretizations in time) applied to nonlinear numerical PDEs, including PDEs with algebraic constraints. Several new linearizations of the nonlinear IRK equations are developed, offering faster and more robust convergence than the often-considered simplified Newton, as well as an effective preconditioner for the true Jacobian if exact Newton iterations are desired. Inverting these linearizations requires solving a set of block 2x2 systems. Under quite general assumptions, it is proven that the preconditioned 2x2 operator's condition number is bounded by a small constant close to one, independent of the spatial discretization, spatial mesh, and time step, and with only weak dependence on the number of stages or integration accuracy. Moreover, the new method is built using the same preconditioners needed for backward Euler-type time stepping schemes, so can be readily added to existing codes. The new methods are applied to several challenging fluid flow problems, including the compressible Euler and Navier Stokes equations, and the vorticity-streamfunction formulation of the incompressible Euler and Navier Stokes equations. Up to 10th-order accuracy is demonstrated using Gauss IRK, while in all cases 4th-order Gauss IRK requires roughly half the number of preconditioner applications as required by standard SDIRK methods.
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks.
Multiple Modes of the Perpendicular Magnetization Switching Scheme within a Single Spin Orbit Torque Device

Spin orbit torque (SOT) has been considered as one of the promising technologies for the next-generation magnetic random access memory (MRAM). So far, SOT has been widely utilized for inducing various modes of magnetization switching. However, it is challenging to integrate multiple modes of magnetization switching together. Here we propose a method for implementing both unipolar and bipolar switching of the perpendicular magnetization within a single SOT device. The mode of switching could be easily altered by tuning the amplitude of the applied current. We show that the field-like torque plays an important role in the switching process. The field-like torque induces the precession of the magnetization in the case of unipolar switching, whereas it helps to generate an effective z-component torque in the case of bipolar switching. In addition, the influence of key parameters on the mode of switching is discussed, including the field-like torque strength, the bias field and the current density. Our proposal could be used to design novel reconfigurable logic circuits in the near future.
Central limit theorem for a birth–growth model with poisson arrivals and random growth speed

We consider Gaussian approximation in a variant of the classical Johnson–Mehl birth–growth model with random growth speed. Seeds appear randomly in $\mathbb{R}^d$ at random times and start growing instantaneously in all directions with a random speed. The locations, birth times, and growth speeds of the seeds are given by a Poisson process. Under suitable conditions on the random growth speed, the time distribution, and a weight function $h\;:\;\mathbb{R}^d \times [0,\infty) \to [0,\infty)$ , we prove a Gaussian convergence of the sum of the weights at the exposed points, which are those seeds in the model that are not covered at the time of their birth. Such models have previously been considered, albeit with fixed growth speed. Moreover, using recent results on stabilization regions, we provide non-asymptotic bounds on the distance between the normalized sum of weights and a standard Gaussian random variable in the Wasserstein and Kolmogorov metrics.
Gromov--Witten theory of $[\mathbb{C}^2/\mathbb{Z}_{n+1}]\times \mathbb{P}^1$

We compute the relative orbifold Gromov–Witten invariants of [C/Zn+1]× P , with respect to vertical fibers. Via a vanishing property of the Hurwitz–Hodge bundle, 2-point rubber invariants are calculated explicitly using Pixton’s formula for the double ramification cycle, and the orbifold quantum Riemann–Roch. As a result parallel to its crepant resolution counterpart for An, the GW/DT/Hilb/Sym correspondence is established for [C/Zn+1]. The computation also implies the crepant resolution conjecture for relative orbifold Gromov–Witten theory of [C/Zn+1]× P .
Understanding and Reducing Biases in Elite Beliefs About the Electorate

o be responsive, politicians have to rely on beliefs about public will. Previous research suggests that perceptions of public opinion are often distorted. However, it remains unclear (1) why reelection-seeking officials misperceive public preferences and (2) how to mitigate these distorted beliefs. I argue that misperceptions result from unequal exposure to different subconstituencies and a tendency of legislators to project their own preferences on voters. I find support for these arguments in a six-wave panel of Swedish MPs combined with mass surveys. Elite beliefs disproportionately reflect the preferences of privileged voters and the personal positions of legislators. Additionally, an experiment with Swiss representatives leveraging real political events reveals how misperceptions can be reduced by encouraging a more balanced exposure to voters. The study concludes that economic and political inequalities are rooted in elite beliefs about the electorate and reveals ways to bolster the links between voters and their representatives.
A proof of Gromov's cube inequality on scalar curvature

Gromov proved a cube inequality on the bound of distances between opposite faces of a cube equipped with a positive scalar curvature metric in dimension $\leq 8$ using minimal surface method. He conjectured that the cube inequality also holds in dimension $\geq 9$. In this paper, we prove Gromov's cube inequality in all dimensions with the optimal constant via Dirac operator method. In fact, our proof yields a strengthened version of Gromov's cube inequality, which does not seem to be accessible by minimal surface method.
Online Nash Social Welfare Maximization with Predictions

We consider the problem of allocating a set of divisible goods to $N$ agents in an online manner, aiming to maximize the Nash social welfare, a widely studied objective which provides a balance between fairness and efficiency. The goods arrive in a sequence of $T$ periods and the value of each agent for a good is adversarially chosen when the good arrives. We first observe that no online algorithm can achieve a competitive ratio better than the trivial $O(N)$, unless it is given additional information about the agents' values. Then, in line with the emerging area of"algorithms with predictions", we consider a setting where for each agent, the online algorithm is only given a prediction of her monopolist utility, i.e., her utility if all goods were given to her alone (corresponding to the sum of her values over the $T$ periods). Our main result is an online algorithm whose competitive ratio is parameterized by the multiplicative errors in these predictions. The algorithm achieves a competitive ratio of $O(\log N)$ and $O(\log T)$ if the predictions are perfectly accurate. Moreover, the competitive ratio degrades smoothly with the errors in the predictions, and is surprisingly robust: the logarithmic competitive ratio holds even if the predictions are very inaccurate. We complement this positive result by showing that our bounds are essentially tight: no online algorithm, even if provided with perfectly accurate predictions, can achieve a competitive ratio of $O(\log^{1-\epsilon} N)$ or $O(\log^{1-\epsilon} T)$ for any constant $\epsilon>0$.
Empirical Low-Altitude Air-to-Ground Spatial Channel Characterization for Cellular Networks Connectivity

Cellular-connected unmanned aerial vehicles (UAVs) have recently attracted a surge of interest in both academia and industry. Understanding the air-to-ground (A2G) propagation channels is essential to enable reliable and/or high-throughput communications for UAVs and protect the ground user equipments (UEs). In this contribution, a recently conducted measurement campaign for the A2G channels is introduced. A uniform circular array (UCA) with 16 antenna elements was employed to collect the downlink signals of two different Long Term Evolution (LTE) networks, at the heights of 0-40m in three different, namely rural, urban and industrial scenarios. The channel impulse responses (CIRs) have been extracted from the received data, and the spatial, including angular, parameters of the multipath components in individual channels were estimated according to a high-resolution-parameter-estimation (HRPE) principle. Based on the HRPE results, clusters of multipath components were further identified. Finally, comprehensive spatial channel characteristics were investigated in the composite and cluster levels at different heights in the three scenarios.
Why Can’t We Learn Foreign Languages in Turkey? Some Psychological Reasons Behind It

Foreign language learning is one of the most significant endeavours for people in all countries in the world. Turkey has given importance to foreign language learning for years. Nevertheless, it has been an unresolved problem for Turkish people. The purpose of the present study is to determine why people fail to learn foreign languages in Turkey and the psychological reasons behind it by focussing on perception, foreign language anxiety and learned helplessness. The participants were 100 volunteer students (56 female, 44 male) who were selected randomly from different faculties and departments at Bursa Uludag University. The data were collected from a questionnaire, which consisted of five main sections to identify participants’ background, perception of foreign language learning, level of foreign language anxiety, and level of learned helplessness. Both quantitative and qualitative data were collected. The data were analysed in relation to the research questions that guided the study. The results indicated that the participants’ perceptions of foreign language learning were positive owing to past positive experiences, but their level of foreign language anxiety was high due to lack of practice in language skills in the past; and their level of learned helplessness was low because of the positive perceptions they possessed. In addition, the findings suggested that the four language skills should be taken into consideration when the contents of foreign language lessons at schools and foreign language courses at universities are prepared and determined.
Title: Assessment of antimicrobial use and prescribing practices among pediatric inpatients in Zimbabwe Authors:

: 147 Abstract This study aims to assess antimicrobial consumption in the pediatric department of a tertiary care public hospital in Zimbabwe. Clinical records of pediatric inpatients admitted to Harare Central Hospital over a 3-week period were reviewed prospectively. Antimicrobial consumption was described as days of therapy per 100 inpatient days (DOT/100 PD). Adherence of antimicrobial drug prescriptions to the National Guidelines was also evaluated. A total of 121 (93.1%) children were prescribed at least one antimicrobial out of 130 children admitted. The median age was 14 months (IQR: 3 – 48 months). Overall antimicrobial consumption was 155.4 DOT / 100 PD (95% CI 146-165.2). The most frequently prescribed antimicrobials were benzylpenicillin, gentamicin and ceftriaxone. Prescriptions were adherent to national guidelines in 57.7% of children. This study shows that there is high antimicrobial drug usage in hospitalized children in Zimbabwe and a considerable proportion of prescriptions are non-adherent with national guidelines.
DISCUSSION OF HYBRID NEW KEYNESIAN PHILLIPS CURVE METHODOLOGY ON INFLATION DYNAMICS

The new Keynesian Phillips curve accepts forward pricing and real marginal costs as the dynamics of inflation. While some firms change prices in every period, the rest of the firms keep prices constant. When fixed prices are evaluated as the lagged value of inflation, price inertia emerges. To prevent this, the hybrid model has been developed by adding backward pricing to the New Keynesian Phillips curve. In this study, while the methodological development of the hybrid model was examined technically, the statistical significance of these parameters was questioned. Thus, the limits of the hybrid model were discussed in explaining inflation dynamics.