Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of church leaders on the integration of migrant youth into South Africa : The case of refugees in the refugee camps managed by churches at Musina

Migration is both an internal and international problem, which needs the attention of various disciplines to address it. There are various motives and reasons for migration of people from their home countries (Browne 2017; Devictor 2016). For instance, some are running way from conflicts, while others migrate for better jobs to help their families and provide them with remittances. People, whether internally or internationally, migrate in order to improve their living circumstances. Rugunanan and Smit (2011) postulated that for many individuals and families across the globe, migration has become a survival strategy for escaping armed conflict and human rights violations in their countries of origin. People who migrate are usually the youth. Southern Africa has now became a region on the move (ed. McDonald 2000), particularly amongst the youth. In the past 21 years, an increasing number of people have migrated to escape poverty, seek livelihoods or escape from political upheavals and civil strife (Thet 2014), such as the Mozambican and Angolan civil wars, the effects of which continue to be felt to date on regional development. The patterns and scale of these population movements are constantly in flux. The economic and political collapse of Zimbabwe, for instance, has generated unprecedented outward migration to Southern African countries (Lee 2003; Oyelana 2016). Even as stability gradually returns in Zimbabwe, the humanitarian crises facing its diaspora and the potential for further waves of migration remain high (Adepoju 1995; Castles, De Haas & Miller 2013; Chimni 2009; Crush, Williams & Peberdy 2005; Wolf 2010).
NOMA for Next-Generation Massive IoT: Performance Potential and Technology Directions

Broader applications of the Internet of Things (loT) are expected in the forthcoming 6G system, although massive loT is already a key scenario in 5G, predominantly relying on physical layer solutions inherited from 4G LTE and primarily using orthogonal multiple access (OMA). In 6G loT, supporting a massive number of connections will be required for diverse services of the vertical sectors, prompting fundamental studies on how to improve the spectral efficiency of the system. One of the key enabling technologies is non-or-thogonal multiple access (NOMA). This article consists of two parts. In the first part, finite block length theory and the diversity order of multi-user systems are used to show the significant potential of NOMA compared to traditional OMA. The supremacy of NOMA over OMA is particularly pronounced for asynchronous contention-based systems relying on imperfect link adaptation, which are commonly assumed for massive loT systems. To approach these performance bounds, in the second part of the article, several promising technology directions are proposed for 6G massive loT, including linear spreading, joint spreading and modulation, multi-user channel coding in the context of various techniques for practical uncoordinated transmissions, cell-free operations, and so on, from the perspective of NOMA.
Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop

Accurate classiﬁcation and localization of abnormalities in chest X-rays play an important role in clinical diagnosis and treatment planning. Building a highly accurate predictive model for these tasks usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their domain knowledge, as well as multi-modality information, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised cross-modal contrastive learning framework, that simultaneously performs disease classi-ﬁcation and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as an auxiliary modality. Speciﬁcally, we ﬁrst apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic modality features to mutually reinforce each other. Their contrasting yields cross-modality representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both clas-siﬁcation and localization tasks. Speciﬁcally, the average classiﬁcation AUCs improve from 82.8% to 83.8%, while the localization results are signiﬁcantly boosted by an average of 2% over different IoU thresholds.
Novel logarithmic corrections to black hole entropy

For a thermodynamic system, apart from thermal fluctuations, there are also fluctuations in thermodynamic volume when the system is in contact with a volume reservoir. For the case of black holes in anti-de Sitter spacetimes, the effect of thermal fluctuations on the entropy is well studied. The aim of this work is to compute novel logarithmic corrections to black hole entropy coming from simultaneous fluctuations of energy and thermodynamic volume. We work in the isothermal-isobaric ensemble and first obtain a general form of corrections to entropy which are valid for any thermodynamic system. Applying the formalism to Kerr black holes in AdS reveals that the black hole entropy gets corrected as: S=S0−klnS0+⋯ where S 0 is given by the Bekenstein–Hawking formula and k = −1. The same leading coefficient is also obtained in the canonical ensemble, i.e. by considering energy fluctuations alone. This coefficient is found to be unaltered in the slowly rotating and high temperature limits.
On the multiwavelength variability of Mrk 110: two components acting at different time-scales

We present the first intensive continuum reverberation mapping study of the high accretionrate Seyfert galaxy Mrk 110. The source was monitored almost daily for more than 200 days with the Swift X-ray and UV/optical telescopes, supported by ground-based observations from Las Cumbres Observatory, the Liverpool Telescope, and the Zowada Observatory, thus extending the wavelength coverage to 9100 Å. Mrk 110 was found to be significantly variable at all wavebands. Analysis of the intraband lags reveals two different behaviours, depending on the timescale. On timescales shorter than 10 days the lags, relative to the shortest UV waveband (∼ 1928 Å), increase with increasing wavelength up to a maximum of ∼ 2d lag for the longest waveband (∼ 9100 Å), consistent with the expectation from disc reverberation. On longer timescales, however, the g-band lags the Swift BAT hard X-rays by ∼ 10 days, with the z-band lagging the g-band by a similar amount, which cannot be explained in terms of simple reprocessing from the accretion disc. We interpret this result as an interplay between the emission from the accretion disc and diffuse continuum radiation from the broad line region.
Erosion and Accretion by Cratering Impacts on Rocky and Icy Bodies

During planet formation, numerous small impacting bodies result in cratering impacts on large target bodies. A fraction of the target surface is eroded, while a fraction of the impactor material accretes onto the surface. These fractions depend upon the impact velocities, the impact angles, and the escape velocities of the target. This study uses smoothed particle hydrodynamics simulations to model cratering impacts onto a planar icy target for which gravity is the dominant force and material strength is neglected. By evaluating numerical results, scaling laws are derived for the escape mass of the target material and the accretion mass of the impactor material onto the target surface. Together with recently derived results for rocky bodies in a companion study, a conclusion is formulated that typical cratering impacts on terrestrial planets, except for those on Mercury, led to a net accretion, while those on the moons of giant planets, e.g., Rhea and Europa, led to a net erosion. Our newly derived scaling laws would be useful for predicting the erosion of the target body and the accretion of the impactor for a variety of cratering impacts that would occur on large rocky and icy planetary bodies during planet formation and collisional evolution from ancient times to today.
Ultrafast measurements of the interfacial spin Seebeck effect in Au and rare-earth iron-garnet bilayers

We investigate picosecond spin-currents across Au/iron-garnet interfaces in response to ultrafast laser heating of the electrons in the Au film. In the picoseconds after optical heating, interfacial spin currents occur due to an interfacial temperature difference between electrons in the metal and magnons in the insulator. We report measurements of this interfacial longitudinal spin Seebeck effect between Au and rare-earth iron-garnet insulators, i.e. RE3Fe5O12, where RE is Y, Eu, Tb, Tm. We use time domain thermoreflectance (TDTR) measurements to characterize the thermal response of the bilayer to ultrafast optical heating. We use timeresolved magneto-optic Kerr effect (TR-MOKE) measurements of the Au layer to measure the time-evolution of spin accumulation in the Au film. We observe a spin Seebeck effect between Au/TmIG that is three times larger than for an Au/YIG bilayer. The interfacial thermal
Spectroscopic and photometric observations of dwarf nova superoutbursts by the 3.8 m telescope Seimei and the Variable Star Network

We present spectroscopic and photometric observations of 17 dwarf-nova superoutbursts obtained by KOOLS-IFU mounted on the 3.8 m telescope Seimei at Okayama Observatory of Kyoto University and through VSNET collaboration. Our spectroscopic observations for six outbursts were performed within 1 d from their optical peak. 11 objects (TCP J00590972+3438357. ASASSN-19ado, TCP J06073081-0101501, ZTF20aavnpug, ASASSN19ady, MASTER OT J061642.05+435617.9, TCP J20034647+1335125, ASASSN-20kv, ASASSN-20kw, MASTER OT J213908.79+161240.2, and ASASSN-20mf) were previously unknown systems, and our observations enabled quick classification of their transient type. These results illustrate that Seimei telescope has the capability to conduct quick follow-up observations of unknown transients. Our photometric observations yielded that 11 objects are WZ Sge-type dwarf novae and their candidates, and the other six objects are SU UMa-type dwarf novae and their candidates. The He II 4686Å emission line was clearly detected among ASASSN-19ado, TCP J06073081-0101501 and MASTER OT J213908.79+161240.2, whose association with a spiral arm structure in an accretion disk has been suggested in the previous studies. Our result suggests that a higher-inclination system shows a stronger emission line of He II 4686Å, as well as larger-amplitude early superhumps.
Fine-Grained Attention for Weakly Supervised Object Localization

Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts. In this paper, we propose a novel residual fine-grained attention (RFGA) module that autonomously excites the less activated regions of an object by utilizing information distributed over channels and locations within feature maps in combination with a residual operation. To be specific, we devise a series of mechanisms of triple-view attention representation, attention expansion, and feature calibration. Unlike other attention-based WSOL methods that learn a coarse attention map, having the same values across elements in feature maps, our proposed RFGA learns fine-grained values in an attention map by assigning different attention values for each of the elements. We validated the superiority of our proposed RFGA module by comparing it with the recent methods in the literature over three datasets. Further, we analyzed the effect of each mechanism in our RFGA and visualized attention maps to get insights.
Blow-up rate and local uniqueness for fractional Schr\"odinger equations with nearly critical growth

We study quantitative aspects and concentration phenomena for ground states of the following nonlocal Schrödinger equation (−∆)u+ V (x)u = u ∗ s −1−ǫ in R , where ǫ > 0, s ∈ (0, 1), 2s := 2N N−2s , N > 4s. We show that the ground state uǫ blows up and precisely with the following rate ‖uǫ‖L∞(RN ) ∼ ǫ − 4s , as ǫ → 0. We also localize the concentration points and, in the case of radial potentials V , we prove local uniqueness of sequences of ground states which exhibit a concentrating behavior.
Two‐Fluid Treatment of Whistling Behavior and the Warm Appleton‐Hartree Extension

As an application of the completely general, ideal two‐fluid analysis of waves in a warm ion‐electron plasma, where six unique wave pair labels (S, A, F, M, O, and X) were identified, we here connect to the vast body of literature on whistler waves. We show that all six mode pairs can demonstrate whistling behavior, when we allow for whistling of both descending and ascending frequency types, and when we study the more general case of oblique propagation to the background magnetic field. We show how the general theory recovers all known approximate group speed expressions for both classical whistlers and ion cyclotron whistlers, which we here extend to include ion contributions and deviations from parallel propagation. At oblique angles and at perpendicular propagation, whistlers are investigated using exact numerical evaluations of the two‐fluid dispersion relation and their group speeds under Earth's magnetosphere conditions. This approach allows for a complete overview of all whistling behavior and we quantify the typical frequency ranges where they must be observable. We use the generality of the theory to show that pair plasmas in pulsar magnetospheres also feature whistling behavior, although not of the classical type at parallel propagation. Whistling of the high‐frequency modes is discussed as well, and we give the extension of the Appleton‐Hartree relation for cold plasmas, to include the effect of a nonzero thermal electron velocity. We use it to quantify the Faraday rotation effect at all angles, and compare its predictions between the cold and warm Appleton‐Hartree equation.
The rotation of selected globular clusters and the differential rotation of M3 in multiple populations from the SDSS-IV APOGEE-2 survey

In this paper, we analyze 10 globular clusters in order to measure their rotational properties by using high precision radial velocity data from the SDSS-IV APOGEE-2 survey. Out of the 10 clusters we were able to successfully measure the rotation speed and position angle of the rotation axis for 9 clusters (M2, M3, M5, M12, M13, M15, M53, M92, M107). The comparison between our results and previous ones shows a really good agreement within our uncertainties. For four of the globular clusters, M3, M13, M5 and M15, we separated the sample into two generation of stars using their [Al/Fe] abundances and examined the kinematic features of these generations separately from one another. In case of M3, we found significant difference between the rotational properties of first and second populations, confirming for the first time the predictions of several numerical simulations from the literature. The other three clusters (M5, M13, M15) also show smaller deviation between the two groups of stars, but those deviations are comparable to our errors.
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
LocalViT: Bringing Locality to Vision Transformers

We study how to introduce locality mechanisms into vision transformers. The transformer network originates from machine translation and is particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking a locality mechanism for information exchange within a local region. Yet, locality is essential for images since it pertains to structures like lines, edges, shapes, and even objects. We add locality to vision transformers by introducing depth-wise convolution into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept. In particular, for ImageNet2012 classification, the locality-enhanced transformers outperform the baselines DeiT-T and PVT-T by 2.6\% and 3.1\% with a negligible increase in the number of parameters and computational effort. Code is available at \url{this https URL}.
A Moment-Based Safety Analysis for Stochastic Dynamical Systems

—Given a stochastic dynamical system modelled via stochastic differential equations (SDEs), we evaluate the safety of the system through characterisations of its exit time moments. We lift the (possibly nonlinear) dynamics into the space of the occupation and exit measures to obtain a set of linear evolution equations which depend on the inﬁnitesimal generator of the SDE. Coupled with appropriate semideﬁnite positive matrix constraints, this yields a moment-based approach for the computation of exit time moments of SDEs with polynomial drift and diffusion dynamics. To extend the capability of the moment approach, we propose a state augmentation method which allows us to generate the evolution equations for a broader class of nonlinear stochastic systems and apply the moment method to previously unsupported dynamics. In particular, we show a general augmentation strategy for sinusoidal dynamics which can be found in most physical systems. We employ the methodology on an Ornstein-Uhlenbeck process and stochastic spring-mass-damper model to characterise their safety via their expected exit times and show the additional exit distribution insights that are afforded through higher order moments.
Generalization bounds via distillation

This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds. The main contribution is an analysis showing that the original network inherits this good generalization bound from its distillation, assuming the use of well-behaved data augmentation. This bound is presented both in an abstract and in a concrete form, the latter complemented by a reduction technique to handle modern computation graphs featuring convolutional layers, fully-connected layers, and skip connections, to name a few. To round out the story, a (looser) classical uniform convergence analysis of compression is also presented, as well as a variety of experiments on cifar and mnist demonstrating similar generalization performance between the original network and its distillation.
Lower Bounds for Maximum Weighted Cut

While there have been many results on lower bounds for Max Cut in unweighted graphs, the only lower bound for non-integer weights is that by Poljak and Turz{\'{\i}}k (1986). In this paper, we launch an extensive study of lower bounds for Max Cut in weighted graphs. We introduce a new approach for obtaining lower bounds for Weighted Max Cut. Using it, Probabilistic Method, Vizing's chromatic index theorem, and other tools, we obtain several lower bounds for arbitrary weighted graphs, weighted graphs of bounded girth and triangle-free weighted graphs. We pose conjectures and open questions.
An Approach to Symbolic Regression Using Feyn

In this article we introduce the supervised machine learning tool called Feyn. The simulation engine that powers this tool is called the QLattice. The QLattice is a supervised machine learning tool inspired by Richard Feynman's path integral formulation, that explores many potential models that solves a given problem. It formulates these models as graphs that can be interpreted as mathematical equations, allowing the user to completely decide on the trade-off between interpretability, complexity and model performance. We touch briefly upon the inner workings of the QLattice, and show how to apply the python package, Feyn, to scientific problems. We show how it differs from traditional machine learning approaches, what it has in common with them, as well as some of its commonalities with symbolic regression. We describe the benefits of this approach as opposed to black box models. To illustrate this, we go through an investigative workflow using a basic data set and show how the QLattice can help you reason about the relationships between your features and do data discovery.