Miracle-dz/UniFAR-FTUs huggingface dataset:
Filament Leg–Leg Reconnection as a Source of Prominent Supra-arcade Downflows

We report on the interaction of the legs of the erupting filament of 2012 August 31 and associated prominent supra-arcade downflows (P-SADs) as observed by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. We employ a number of image processing techniques to enhance weak interacting features. As the filament erupts, both legs stretch outwards. The positive-polarity leg also untwists and splits into two parts. The first part runs into the conjugate (negative-polarity) leg, tearing it apart. The second part then converges into the remnant of the conjugate leg, after which both weaken and finally disappear. All these episodes of interaction of oppositely oriented filament legs are followed by the appearance of P-SADs, seen in the on-disk projection to be shaped as loop tops, along with many weaker SADs. All SADs are preceded by hot supra-arcade downflowing loops. This observed evolution is consistent with the three-dimensional rr–rf (leg–leg) reconnection, where the erupting flux rope reconnects with itself. In our observations, as well as in some models, the reconnection in this geometry is found to be long lasting. It plays a substantial role in the evolution of the flux rope of the erupting filament and leads to prominent SADs.
Isotropic Representation Can Improve Dense Retrieval

The recent advancement in language representation modeling has broadly affected the design of dense retrieval models. In particular, many of the high-performing dense retrieval models evaluate representations of query and document using BERT, and subsequently apply a cosine-similarity based scoring to determine the relevance. BERT representations, however, are known to follow an anisotropic distribution of a narrow cone shape and such an anisotropic distribution can be undesirable for the cosine-similarity based scoring. In this work, we first show that BERT-based DR also follows an anisotropic distribution. To cope with the problem, we introduce unsupervised post-processing methods of Normalizing Flow and whitening, and develop token-wise method in addition to the sequence-wise method for applying the post-processing methods to the representations of dense retrieval models. We show that the proposed methods can effectively enhance the representations to be isotropic, then we perform experiments with ColBERT and RepBERT to show that the performance (NDCG at 10) of document re-ranking can be improved by 5.17\%$\sim$8.09\% for ColBERT and 6.88\%$\sim$22.81\% for RepBERT. To examine the potential of isotropic representation for improving the robustness of DR models, we investigate out-of-distribution tasks where the test dataset differs from the training dataset. The results show that isotropic representation can achieve a generally improved performance. For instance, when training dataset is MS-MARCO and test dataset is Robust04, isotropy post-processing can improve the baseline performance by up to 24.98\%. Furthermore, we show that an isotropic model trained with an out-of-distribution dataset can even outperform a baseline model trained with the in-distribution dataset.
The Shifting Attention of Political Leaders: Evidence from Two Centuries of Presidential Speeches

We use natural-language-processing algorithms on a novel dataset of over 900 presidential speeches from ten Latin American countries spanning two centuries to study the dynamics and determinants of presidential policy priorities. We show that most speech content can be characterized by a compact set of policy issues whose relative composition exhibited slow yet substantial shifts over 1819-2022. Presidential attention initially centered on military interventions and the development of state capacity. Attention gradually evolved towards building physical capital through investments in infrastructure and public services and finally turned towards building human capital through investments in education, health, and social safety nets. We characterize the way in which president-level characteristics, like age and gender, predict the main policy issues. Our findings offer novel insights into the dynamics of presidential attention and the factors that shape it, expanding our understanding of political agenda-setting.
Correlated 1–1000 Hz magnetic field fluctuations from lightning over Earth-scale distances and their impact on gravitational wave searches

We report Earth-scale distance magnetic correlations from lightning strokes in the frequency range 1-1000 Hz at several distances ranging from 1100 to 9000 km. Noise sources which are correlated on Earth-scale distances can affect future searches for gravitational-wave signals with ground-based gravitational-wave interferometric detectors. We consider the impact of correlations from magnetic field fluctuations on gravitational-wave searches due to Schumann resonances ($<$50 Hz) as well as higher frequencies ($>$100 Hz). We demonstrate that individual lightning strokes are a likely source for the observed correlations in the magnetic field fluctuations at gravitational-wave observatories and discuss some of their characteristics. Furthermore, we predict their impact on searches for an isotropic gravitational-wave background, as well as for searches looking for short-duration transient gravitational waves, both unmodeled signals (bursts) as well as modeled signals (compact binary coalescence). Whereas the recent third observing run by LIGO and Virgo was free of an impact from correlated magnetic field fluctuations, future runs could be affected. For example, at current magnetic coupling levels, neutron star inspirals in third generation detectors are likely to be contaminated by multiple correlated lightning glitches. We suggest that future detector design should consider reducing lightning coupling by, for example, reducing the lightning-induced beam tube currents that pass through sensitive magnetic coupling regions in current detectors. We also suggest that the diurnal and seasonal variation in lightning activity may be useful in discriminating between detector correlations that are produced by gravitational waves and those produced by lightning.
Monocular Camera-Based Complex Obstacle Avoidance via Efficient Deep Reinforcement Learning

Deep reinforcement learning has achieved great success in laser-based collision avoidance works because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to deploy for a large scale of robots but also demonstrate unsatisfactory robustness towards the complex obstacles, including irregular obstacles, e.g., tables, chairs, and shelves, as well as complex ground and special materials. In this paper, we propose a novel monocular camera-based complex obstacle avoidance framework. Particularly, we innovatively transform the captured RGB images to pseudo-laser measurements for efficient deep reinforcement learning. Compared to the traditional laser measurement captured at a certain height that only contains one-dimensional distance information away from the neighboring obstacles, our proposed pseudo-laser measurement fuses the depth and semantic information of the captured RGB image, which makes our method effective for complex obstacles. We also design a feature extraction guidance module to weight the input pseudo-laser measurement, and the agent has more reasonable attention for the current state, which is conducive to improving the accuracy and efficiency of the obstacle avoidance policy. Besides, we adaptively add the synthesized noise to the laser measurement during the training stage to decrease the sim-to-real gap and increase the robustness of our model in the real environment. Finally, the experimental results show that our framework achieves state-of-the-art performance in several virtual and real-world scenarios.
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets

Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation and some surprisingly never even observe an inverse correlation indicative of a necessary trade-off. The possibility of inverse patterns is important to determine whether ID performance can serve as a proxy for OOD generalization capabilities. This paper shows with multiple datasets that inverse correlations between ID and OOD performance do happen in real-world data - not only in theoretical worst-case settings. We also explain theoretically how these cases can arise even in a minimal linear setting, and why past studies could miss such cases due to a biased selection of models. Our observations lead to recommendations that contradict those found in much of the current literature. - High OOD performance sometimes requires trading off ID performance. - Focusing on ID performance alone may not lead to optimal OOD performance. It may produce diminishing (eventually negative) returns in OOD performance. - In these cases, studies on OOD generalization that use ID performance for model selection (a common recommended practice) will necessarily miss the best-performing models, making these studies blind to a whole range of phenomena.
Efficient preconditioners for solving dynamical optimal transport via interior point methods

In this paper we address the numerical solution of the quadratic optimal transport problem in its dynamical form, the so-called Benamou-Brenier formulation. When solved using interior point methods, the main computational bottleneck is the solution of large saddle point linear systems arising from the associated Newton-Raphson scheme. The main purpose of this paper is to design efficient preconditioners to solve these linear systems via iterative methods. Among the proposed preconditioners, we introduce one based on the partial commutation of the operators that compose the dual Schur complement of these saddle point linear systems, which we refer as $\boldsymbol{B}\boldsymbol{B}$-preconditioner. A series of numerical tests show that the $\boldsymbol{B}\boldsymbol{B}$-preconditioner is the most efficient among those presented, despite a performance deterioration in the last steps of the interior point method. It is in fact the only one having a CPU-time that scales only slightly worse than linearly with respect to the number of unknowns used to discretize the problem.
Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases

Automatic extraction of funding information from academic articles adds significant value to industry and research communities, including tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. Two major challenges of identifying and linking funding entities are: (i) sparse graph structure of the Knowledge Base (KB), which makes the commonly used graph-based entity linking approaches suboptimal for the funding domain, (ii) missing entities in KB, which (unlike recent zero-shot approaches) requires marking entity mentions without KB entries as NIL. We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. Our model builds on a transformer-based mention detection and a bi-encoder model to perform entity linking. We show that our model outperforms strong existing baselines.
Evolution of Close Binary System Parameter Distributions

In this paper, we investigate the orbital and stellar parameters of low- and intermediate-mass close binary systems. We use models, presented in the catalog of Han et al. and calculate parameters of accretors. We also construct distributions of systems along luminosity, semimajor axis and angular momentum, and make some conclusions on their evolution with time. We compare the results with observational data and it shows a good agreement. The set of theoretical models published quite adequately describes the observational data and, consequently, can be used to determine the evolutionary path of specific close binary systems, their initial parameter values and final stages.
Detection of anisotropic satellite quenching in galaxy clusters up to z ∼ 1

Satellite galaxies in the cluster environment are more likely to be quenched than galaxies in the general field. Recently, it has been reported that satellite galaxy quenching depends on the orientation relative to their central galaxies: satellites along the major axis of centrals are more likely to be quenched than those along the minor axis. In this paper, we report a detection of such anisotropic quenching up to z ∼ 1 based on a large optically selected cluster catalogue constructed from the Hyper Suprime-Cam Subaru Strategic Program. We calculate the quiescent satellite galaxy fraction as a function of orientation angle measured from the major axis of central galaxies and find that the quiescent fractions at 0.25 < z < 1 are reasonably fitted by sinusoidal functions with amplitudes of a few per cent. Anisotropy is clearer in inner regions (<r200m) of clusters and not significant in cluster outskirts (>r200m). We also confirm that the observed anisotropy cannot be explained by differences in local galaxy density or stellar mass distribution along the two axes. Quiescent fraction excesses between the two axes suggest that the quenching efficiency contributing to the anisotropy is almost independent of stellar mass, at least down to our stellar mass limit of $M_{*}=1\times 10^{10}\, {\rm M}_{\odot }$. Finally, we argue that the physical origins of the observed anisotropy should have shorter quenching time-scales than $\sim 1\, \mathrm{Gyr}$, like ram-pressure stripping, because, for anisotropic quenching to be observed, satellites must be quenched before their initial orientation angles are significantly changed.
Adversarial for Social Privacy: A Poisoning Strategy to Degrade User Identity Linkage

—Privacy issues on social networks have been ex- tensively discussed in recent years. The user identity linkage (UIL) task, aiming at ﬁnding corresponding users across different social networks, would be a threat to privacy if unethically applied. The sensitive user information might be detected through connected identities. A promising and novel solution to this issue is to design an adversarial strategy to degrade the matching performance of UIL models. However, most existing adversarial attacks on graphs are designed for models working in a single network, while UIL is a cross-network learning task. Meanwhile, privacy protection against UIL works unilaterally in real-world scenarios, i.e., the service provider can only add perturbations to its own network to protect its users from being linked. To tackle these challenges, this paper proposes a novel adversarial attack strategy that poisons one target network to prevent its nodes from being linked to other networks by UIL algorithms. Speciﬁcally, we reformalize the UIL problem in the perspective of kernelized topology consistency and convert the attack objective to maximizing the structural changes within the target network before and after attacks. A novel graph kernel is then deﬁned with Earth mover’s distance (EMD) on the edge-embedding space. In terms of efﬁciency, a fast attack strategy is proposed by greedy searching and replacing EMD with its lower bound. Results on three real-world datasets indicate that the proposed attacks can best fool a wide range of UIL models and reach a balance between attack effectiveness and imperceptibility.
Supervised Contrastive Learning with Hard Negative Samples

Unsupervised contrastive learning (UCL) is a self-supervised learning technique that aims to learn a useful representation function by pulling positive samples close to each other while pushing negative samples far apart in the embedding space. To improve the performance of UCL, several works introduced hard-negative unsupervised contrastive learning (H-UCL) that aims to select the"hard"negative samples in contrast to a random sampling strategy used in UCL. In another approach, under the assumption that the label information is available, supervised contrastive learning (SCL) has developed recently by extending the UCL to a fully-supervised setting. In this paper, motivated by the effectiveness of hard-negative sampling strategies in H-UCL and the usefulness of label information in SCL, we propose a contrastive learning framework called hard-negative supervised contrastive learning (H-SCL). Our numerical results demonstrate the effectiveness of H-SCL over both SCL and H-UCL on several image datasets. In addition, we theoretically prove that, under certain conditions, the objective function of H-SCL can be bounded by the objective function of H-UCL but not by the objective function of UCL. Thus, minimizing the H-UCL loss can act as a proxy to minimize the H-SCL loss while minimizing UCL loss cannot. As we numerically showed that H-SCL outperforms other contrastive learning methods, our theoretical result (bounding H-SCL loss by H-UCL loss) helps to explain why H-UCL outperforms UCL in practice.
De nouveaux invariants num'eriques pour les extensions totalement ram-ifi'ees de corps locaux

Resume Nous etudions la categorie des extensions totalement ramifiees d'un corps local a l'aide d'un certain type d'equations, definissant de telles extensions. Ce type d'equations est defini par des series formelles, et non pas par des polynomes d'Eisenstein. A cette occasion, nous ameliorons des resultats anciens de Arf et de Krasner. En particulier, nous donnons de nouveaux invariants numeriques pour les extensions totalement ramifiees d'un corps local qui ameliorent l'information donnee par la fonction de Herbrand.
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach

Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Effective Approach (FEA) method with two learning stages for CRE: 1) Fast Adaption (FA) warms up the model with only new data. 2) Balanced Tuning (BT) finetunes the model on the balanced memory data. Despite its simplicity, FEA achieves comparable (on TACRED or superior (on FewRel) performance compared with the state-of-the-art baselines. With careful examinations, we find that the data imbalance between new and old relations leads to a skewed decision boundary in the head classifiers over the pretrained encoders, thus hurting the overall performance. In FEA, the FA stage unleashes the potential of memory data for the subsequent finetuning, while the BT stage helps establish a more balanced decision boundary. With a unified view, we find that two strong CRE baselines can be subsumed into the proposed training pipeline. The success of FEA also provides actionable insights and suggestions for future model designing in CRE.
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning

While variance reduction methods have shown great success in solving large scale optimization problems, many of them suffer from accumulated errors and, therefore, should periodically require the full gradient computation. In this paper, we present a single-loop algorithm named SLEDGE (Single-Loop mEthoD for Gradient Estimator) for finite-sum nonconvex optimization, which does not require periodic refresh of the gradient estimator but achieves nearly optimal gradient complexity. Unlike existing methods, SLEDGE has the advantage of versatility; (i) second-order optimality, (ii) exponential convergence in the PL region, and (iii) smaller complexity under less heterogeneity of data. We build an efficient federated learning algorithm by exploiting these favorable properties. We show the first and second-order optimality of the output and also provide analysis under PL conditions. When the local budget is sufficiently large and clients are less (Hessian-)~heterogeneous, the algorithm requires fewer communication rounds then existing methods such as FedAvg, SCAFFOLD, and Mime. The superiority of our method is verified in numerical experiments.
Authentication, Authorization, and Selective Disclosure for IoT data sharing using Verifiable Credentials and Zero-Knowledge Proofs

As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever"smarter"by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of"over the top"services, e.g., energy management. Most data sharing techniques follow an"all or nothing"approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.
Some Continuity Properties of Quantum Rényi Divergences

In the problem of binary quantum channel discrimination with product inputs, the supremum of all type II error exponents for which the optimal type I errors go to zero is equal to the Umegaki channel relative entropy, while the infimum of all type II error exponents for which the optimal type I errors go to one is equal to the infimum of the sandwiched channel Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergences over all <inline-formula> <tex-math notation="LaTeX">$\alpha >1$ </tex-math></inline-formula>. We prove the equality of these two threshold values (and therefore the strong converse property for this problem) using a minimax argument based on a newly established continuity property of the sandwiched Rényi divergences. Motivated by this, we give a detailed analysis of the continuity properties of various other quantum (channel) Rényi divergences, which may be of independent interest.