Miracle-dz/UniFAR-FTUs huggingface dataset:
p-d-Separation - A Concept for Expressing Dependence/Independence Relations in Causal Networks

Spirtes, Glymour and Scheines formulated a Conjecture that a direct dependence test and a head-to-head meeting test would suffice to construe directed acyclic graph decompositions of a joint probability distribution (Bayesian network) for which Pearl's d-separation applies. This Conjecture was later shown to be a direct consequence of a result of Pearl and Verma. This paper is intended to prove this Conjecture in a new way, by exploiting the concept of p-d-separation (partial dependency separation). While Pearl's d-separation works with Bayesian networks, p-d-separation is intended to apply to causal networks: that is partially oriented networks in which orientations are given to only to those edges, that express statistically confirmed causal influence, whereas undirected edges express existence of direct influence without possibility of determination of direction of causation. As a consequence of the particular way of proving the validity of this Conjecture, an algorithm for construction of all the directed acyclic graphs (dags) carrying the available independence information is also presented. The notion of a partially oriented graph (pog) is introduced and within this graph the notion of p-d-separation is defined. It is demonstrated that the p-d-separation within the pog is equivalent to d-separation in all derived dags.
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs

Neural networks are commonly used in safety-critical real-world applications. Unfortunately, the predicted output is often highly sensitive to small, and possibly imperceptible, changes to the input data. Proving that either no such adversarial examples exist, or providing a concrete instance, is therefore crucial to ensure safe applications. As enumerating and testing all potential adversarial examples is computationally infeasible, verification techniques have been developed to provide mathematically sound proofs of their absence using overestimations of the network activations. We propose an improved technique for computing tight upper and lower bounds of these node values, based on increased flexibility gained by computing both bounds independently of each other. Furthermore, we gain an additional improvement by re-implementing part of the original state-of-the-art software "Neurify", leading to a faster analysis. Combined, these adaptations reduce the necessary runtime by up to 78%, and allow a successful search for networks and inputs that were previously too complex. Finally, we provide proofs for tight upper and lower bounds on max-pooling layers in convolutional networks. To ensure widespread usability, we open source our implementation "Debona", featuring both the implementation specific enhancements as well as the refined boundary computation for faster and more exact results.
Helium Line Emissivities in the Solar Corona

We present new collisional-radiative models (CRMs) for helium in the quiescent solar corona and predict the emissivities of the He and He+ lines to be observed by DKIST, Solar Orbiter, and Proba-3. We discuss in detail the rates we selected for these models, highlighting several shortcomings we have found in previous work. As no previous complete and self-consistent coronal CRM for helium existed, we have benchmarked our largest model at a density of 106 cm−3 and temperature of 20,000 K against recent CRMs developed for photoionized nebulae. We then present results for the outer solar corona, using new dielectronic recombination rates we have calculated, which increase the abundance of neutral helium by about a factor of 2. We also find that all optical triplet He i lines, and in particular the well-known He i 10830 and 5876 Å lines, are strongly affected by both photoexcitation and photoionization from the disk radiation and that extensive CRMs are required to obtain correct estimates. Close to the Sun, at an electron density of 108 cm−3 and temperature of 1 MK, we predict the emissivity of He i 10830 Å to be comparable to that of the strong Fe xiii coronal line at 10798 Å. However, we expect the He i emissivity to sharply fall in the outer corona, with respect to Fe xiii. We confirm that the He+ Lyα at 304 Å is also significantly affected by photoexcitation and is expected to be detectable as a strong coronal line up to several solar radii.
CryptoNAS: Private Inference on a ReLU Budget

Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs. Recently, researchers have adapted cryptographic techniques to show PI is possible, however all solutions increase inference latency beyond practical limits. This paper makes the observation that existing models are ill-suited for PI and proposes a novel NAS method, named CryptoNAS, for finding and tailoring models to the needs of PI. The key insight is that in PI operator latency cost are non-linear operations (e.g., ReLU) dominate latency, while linear layers become effectively free. We develop the idea of a ReLU budget as a proxy for inference latency and use CryptoNAS to build models that maximize accuracy within a given budget. CryptoNAS improves accuracy by 3.4% and latency by 2.4x over the state-of-the-art.
Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there is a proliferation in the use of machine learning approaches to represent subgrid scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parameterizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a application of the multiscale Lorenz 96 system for which the parameterization model for unresolved scales is exactly known. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term perdition of the underlying chaotic dynamics with our framework compared to using only neural network parameterizations for future prediction. Moreover, we demonstrate that these data-driven parameterization models can handle the non-Gaussian statistics of subgrid scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular non-intrusive way.
Forward doubly-virtual Compton scattering off the nucleon in chiral perturbation theory. II. Spin polarizabilities and moments of polarized structure functions

We examine the polarized doubly-virtual Compton scattering (VVCS) off the nucleon using chiral perturbation theory ($\chi$PT). The polarized VVCS contains a wealth of information on the spin structure of the nucleon which is relevant to the calculation of the two-photon-exchange effects in atomic spectroscopy and electron scattering. We report on a complete next-to-leading-order (NLO) calculation of the polarized VVCS amplitudes $S_1(\nu, Q^2)$ and $S_2(\nu, Q^2)$, and the corresponding polarized spin structure functions $g_1(x, Q^2)$ and $g_2(x,Q^2)$. Our results for the moments of polarized structure functions, partially related to different spin polarizabilities, are compared to other theoretical predictions and "data-driven" evaluations, as well as to the recent Jefferson Lab measurements. By expanding the results in powers of the inverse nucleon mass, we reproduce the known "heavy-baryon" expressions. This serves as a check of our calculation, as well as demonstrates the differences between the manifestly Lorentz-invariant baryon $\chi$PT (B$\chi$PT) and heavy-baryon (HB$\chi$PT) frameworks.
Autofocusing technologies for whole slide imaging and automated microscopy

Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in recent years. Due to the inherent tissue topography variability, accurate autofocusing remains a critical challenge for WSI and automated microscopy systems. The traditional focus map surveying method is limited in its ability to acquire a high degree of focus points while still maintaining high throughput. Real‐time approaches decouple image acquisition from focusing, thus allowing for rapid scanning while maintaining continuous accurate focus. This work reviews the traditional focus map approach and discusses the choice of focus measure for focal plane determination. It also discusses various real‐time autofocusing approaches including reflective‐based triangulation, confocal pinhole detection, low‐coherence interferometry, tilted sensor approach, independent dual sensor scanning, beam splitter array, phase detection, dual‐LED illumination and deep‐learning approaches. The technical concepts, merits and limitations of these methods are explained and compared to those of a traditional WSI system. This review may provide new insights for the development of high‐throughput automated microscopy imaging systems that can be made broadly available and utilizable without loss of capacity.
The initial step towards JOREK integration in IMAS

JOREK is being adapted to work with the Integrated Modelling & Analysis Suite (IMAS) which is being actively developed and used by the ITER Organization, the EUROfusion community and other ITER Members. The list of codes adapted to use the IMAS Data Model is gradually increasing with examples including SOLPS-ITER and JINTRAC. The main goal of the integration of JOREK with IMAS is to enable interaction with the plasma scenarios stored in the IMAS databases in the form of Interface Data Structures (IDSs): input conditions can be read from the databases and nonlinear plasma states determined by JOREK stored. IDSs provide a uniform way of representing data within the IMAS framework and allow to transfer data between codes and to storage within larger integrated modelling workflows. In order to integrate JOREK within IMAS it is therefore necessary that transformation tools are developed to facilitate the reading and writing of the relevant IDSs, including the MHD IDS, with its underlying Generalized Grid Description (GGD). For this purpose, utilities have been developed that extract JOREK simulation plasma state, namely the grid geometry and computed physical quantities for each time slice, and then transform them to the appropriate output IDSs. In this article, these initial steps towards full JOREK integration into IMAS is presented.
BubbleMap: Privilege Mapping for Behavior-Based Implicit Authentication Systems

Leveraging users’ behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as remembering and entering passwords. Various IA schemes have been proposed based on different behavioral and contextual features such as gait, touch, and GPS. However, existing IA schemes suffer from false positives, i.e., falsely accepting an adversary, and false negatives, i.e., falsely rejecting the legitimate user due to users’ behavior change and noise. To deal with this problem, we propose BubbleMap (BMap), a framework that can be seamlessly incorporated into any existing IA system to balance between security (reducing false positives) and usability (reducing false negatives) as well as reducing the equal error rate (EER). To evaluate the proposed framework, we implemented BMap on five state-of-the-art IA systems. We also conducted an experiment in a real-world environment from 2016 to 2020. Most of the experimental results show that BMap can greatly enhance the IA schemes’ performances in terms of the EER, security, and usability, with a small amount of penalty on energy consumption.
Dual-Resolution Correspondence Networks

We tackle the problem of establishing dense pixel-wise correspondences between a pair of images. In this work, we introduce Dual-Resolution Correspondence Networks (DRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. DRC-Net extracts both coarse- and fine- resolution feature maps. The coarse maps are used to produce a full but coarse 4D correlation tensor, which is then refined by a learnable neighbourhood consensus module. The fine-resolution feature maps are used to obtain the final dense correspondences guided by the refined coarse 4D correlation tensor. The selected coarse-resolution matching scores allow the fine-resolution features to focus only on a limited number of possible matches with high confidence. In this way, DRC-Net dramatically increases matching reliability and localisation accuracy, while avoiding to apply the expensive 4D convolution kernels on fine-resolution feature maps. We comprehensively evaluate our method on large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night. It achieves the state-of-the-art results on all of them.
Generalized Energy Based Models

We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the "generator"). GEBMs are trained by alternating between learning the energy and the base. We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base. Samples from the posterior on the latent space of the trained model can be obtained via MCMC, thus finding regions in this space that produce better quality samples. Empirically, the GEBM samples on image-generation tasks are of much better quality than those from the learned generator alone, indicating that all else being equal, the GEBM will outperform a GAN of the same complexity. GEBMs also return state-of-the-art performance on density modelling tasks, and when using base measures with an explicit form.
Signed Graph Metric Learning via Gershgorin Disc Alignment

Given a convex and differentiable objective $Q(\M)$ for a real, symmetric matrix $\M$ in the positive definite (PD) cone---used to compute Mahalanobis distances---we propose a fast general metric learning framework that is entirely projection-free. We first assume that $\M$ resides in a space $\cS$ of generalized graph Laplacian matrices (graph metric matrices) corresponding to balanced signed graphs. Unlike low-rank metric matrices common in the literature, $\cS$ includes the important diagonal-only matrices as a special case. The key theorem to circumvent full eigen-decomposition and enable fast metric matrix optimization is Gershgorin disc alignment (GDA): given graph metric matrix $\M \in \cS$ and diagonal matrix $§$, where $S_{ii} = 1/v_i$ and $\v$ is the first eigenvector of $\M$, we prove that Gershgorin disc left-ends of similar transform $\B = §\M §^{-1}$ are perfectly aligned at the smallest eigenvalue $\lambda_{\min}$. Using this theorem, we replace the PD cone constraint in the metric learning problem with tightest possible linear constraints per iteration, so that the alternating optimization of the diagonal / off-diagonal terms in $\M$ can be solved efficiently as linear programs via Frank-Wolfe iterations. We update $\v$ using Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) with warm start as matrix entries in $\M$ are optimized successively. Experiments show that our graph metric optimization is significantly faster than cone-projection methods, and produces competitive binary classification performance.
Differentially Private Median Forests for Regression and Classification

Random forests are a popular method for classification and regression due to their versatility. However, this flexibility can come at the cost of user privacy, since training random forests requires multiple data queries, often on small, identifiable subsets of the training data. Differentially private approaches based on extremely random trees reduce the number of queries, but can lead to low-occupancy leaf nodes which require the addition of large amounts of noise. In this paper, we propose DiPriMe forests, a novel tree-based ensemble method for regression and classification problems, that ensures differential privacy while maintaining high utility. We construct trees based on a privatized version of the median value of attributes, obtained via the exponential mechanism. The use of the noisy median encourages balanced leaf nodes, ensuring that the noise added to the parameter estimate at each leaf is not overly large. The resulting algorithm, which is appropriate for real or categorical covariates, exhibits high utility while ensuring differential privacy.
Surface Brightness Fluctuations for constraining the chemical enrichment of massive galaxies

Abstract Based on very deep photometry, Surface Brightness Fluctuations (SBF) have been traditionally used to determine galaxy distances. We have recently computed SBF spectra of stellar populations at moderately high resolution, which are fully based on empirical stars. We show that the SBF spectra provide an unprecedented potential for stellar population studies that, so far, have been tackled on the basis of the mean fluxes. We find that the SBFs are able to unveil metal-poor stellar components at the one percent level, which are not possible to disentangle with the standard analysis. As these metal-poor components correspond to the first stages of the chemical enrichment, the SBF analysis provides stringent constrains on the quenching epoch.
An Intelligent Group Event Recommendation System in Social networks

The importance of contexts has been widely recognized in recommender systems for individuals. However, most existing group recommendation models in Event-Based Social Networks (EBSNs) focus on how to aggregate group members' preferences to form group preferences. In these models, the influence of contexts on groups is considered but simply defined in a manual way, which cannot model the complex and deep interactions between contexts and groups. In this paper, we propose an Attention-based Context-aware Group Event Recommendation model (ACGER) in EBSNs. ACGER models the deep, non-linear influence of contexts on users, groups, and events through multi-layer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we propose that the preference of a group need to be obtained from indirect and direct perspectives (called indirect preference and direct preference respectively). In order to obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. In order to obtain the direct preference, we employ neural networks to directly learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EBSNs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on two real datasets from Meetup show that our model ACGER significantly outperforms the state-of-the-art models.
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization

Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal machine translation, and the automatic post-editing of machine translation, require mapping a set of multiple distinct inputs into a single output sequence. Recent work has introduced bespoke architectures for these multi-input settings, and developed models which can handle increasingly longer inputs; however, the performance of special model architectures is limited by the available in-domain training data. In this work we propose a simple decoding methodology which ensembles the output of multiple instances of the same model on different inputs. Our proposed approach allows models trained for vanilla s2s tasks to be directly used in multi-input settings. This works particularly well when each of the inputs has significant overlap with the others, as when compressing a cluster of news articles about the same event into a single coherent summary, and we obtain state-of-the-art results on several multi-document summarization datasets.
The Milky Way’s Shell Structure Reveals the Time of a Radial Collision

We identify shell structures in the Milky Way for the first time. We find two shells in the Virgo Overdensity region and two shells in the Hercules Aquila Cloud region using Sloan Digital Sky Survey, Gaia, and LAMOST data. These shell stars are a subset of the substructure previously identified as the Virgo Radial Merger (VRM). Timing arguments for these shells indicate that their progenitor dwarf galaxy passed through the Galactic center 2.7 ± 0.2 Gyr ago. Based on the time of collision, it is also possible that the VRM is related to the phenomenon that created phase-space spirals in the vertical motion of the disk and/or the Splash and could have caused a burst of star formation in the inner disk. We analyze phase mixing in a collection of radial merger N-body simulations and find that shell structure similar to that observed in Milky Way data disappears by 5 Gyr after collision with the Galactic center. The method used to calculate the merger time of the VRM was able to reliably recover the correct merger times for these simulations. Previous work supports the idea that the VRM and the Gaia Sausage/Gaia–Enceladus Merger are the same. However, the Gaia Sausage is widely believed to be 8–11 Gyr old. The disparate ages could be reconciled if the larger age is associated with an infall time when the progenitor crossed the virial radius; we do not constrain the time at which the progenitor became bound to the Milky Way. Alternatively, the Gaia Sausage could be younger than previously thought.
Conceptual Remote Distance Measurement with a Double-Slit Interference

Distance measurement is crucial to astronomy. Here we suggest a new conceptual method to measure the distance by using a local instrument. By engaging the double-slit interference and by considering the phase information of the light, the position of the intensity maximum is related to the distance of the source. Consequently, the precise measurement of the position can be used to measure the distance of the remote source.