Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive black hole and gas dynamics in galaxy nuclei mergers. I. Numerical implementation

Numerical effects are known to plague adaptive mesh refinement (AMR) codes when treating massive particles, e.g. representing massive black holes (MBHs). In an evolving background, they can experience strong, spurious perturbations and then follow unphysical orbits. We study by means of numerical simulations the dynamical evolution of a pair MBHs in the rapidly and violently evolving gaseous and stellar background that follows a galaxy major merger. We confirm that spurious numerical effects alter the MBH orbits in AMR simulations, and show that numerical issues are ultimately due to a drop in the spatial resolution during the simulation, drastically reducing the accuracy in the gravitational force computation. We therefore propose a new refinement criterion suited for massive particles, able to solve in a fast and precise way for their orbits in highly dynamical backgrounds. The new refinement criterion we designed enforces the region around each massive particle to remain at the maximum resolution allowed, independently upon the local gas density. Such maximallyresolved regions then follow the MBHs along their orbits, and effectively avoids all spurious effects caused by resolution changes. Our suite of high resolution, adaptive mesh-refinement hydrodynamic simulations, including different prescriptions for the sub-grid gas physics, shows that the new refinement implementation has the advantage of not altering the physical evolution of the MBHs, accounting for all the non trivial physical processes taking place in violent dynamical scenarios, such as the final stages of a galaxy major merger.
\psi(2S) production as a function of charged-particle multiplicity in pp collisions at $\sqrt{s}$= 13 TeV and p--Pb collisions at $\sqrt{s_{\mathrm{NN}}}$= 8.16 TeV with ALICE at the LHC

The self-normalised $\psi(2S)$ yields as a function of charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV and p--Pb collisions at $\sqrt{s_{\mathrm{NN}}}$= 8.16 TeV, using the ALICE detector at the CERN LHC, are reported in these proceedings. This measurement represents one of the main tools to understand the role of multiparton interactions and their influence on charmonium production in small systems, i.e pp and pA collisions. The ratio of $\psi(2S)$-over-J/$\psi$ as a function of charged-particle multiplicity is also presented for both pp and p--Pb collisions. The comparison to theoretical calculations is reported.
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again

Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging problems of over-smoothening and information squashing along with conventional difficulty caused by vanishing gradients and over-fitting. Previous works have been primarily focused on the study of over-smoothening and over-squashing phenomena in training deep GCNs. Surprisingly, in comparison with CNNs/RNNs, very limited attention has been given to understanding how healthy gradient flow can benefit the trainability of deep GCNs. In this paper, firstly, we provide a new perspective of gradient flow to understand the substandard performance of deep GCNs and hypothesize that by facilitating healthy gradient flow, we can significantly improve their trainability, as well as achieve state-of-the-art (SOTA) level performance from vanilla-GCNs. Next, we argue that blindly adopting the Glorot initialization for GCNs is not optimal, and derive a topology-aware isometric initialization scheme for vanilla-GCNs based on the principles of isometry. Additionally, contrary to ad-hoc addition of skip-connections, we propose to use gradient-guided dynamic rewiring of vanilla-GCNs} with skip connections. Our dynamic rewiring method uses the gradient flow within each layer during training to introduce on-demand skip-connections adaptively. We provide extensive empirical evidence across multiple datasets that our methods improve gradient flow in deep vanilla-GCNs and significantly boost their performance to comfortably compete and outperform many fancy state-of-the-art methods. Codes are available at: https://github.com/VITA-Group/GradientGCN.
DETECTION OF A LIGHT ECHO FROM THE OTHERWISE NORMAL SN 2007af

We present the discovery of a light echo from SN 2007af, a normal Type Ia supernova (SN Ia) in NGC 5584. Hubble Space Telescope images taken three years post explosion reveal two separate echoes: an outer echo and an extended central region, which we propose to be an inner echo for which details are unresolved. Multiple images were obtained in the F160W, F350LP, F555W, and F814W using the Wide Field Camera 3. If the outer echo is produced by an interstellar dust sheet perpendicular to the line of sight, it is located ∼800 pc in front of the SN. The dust for the inner echo is 0.45 pc < d < 90 ? > pc away from the SN. The inner echo color is consistent with typical interstellar dust wavelength-dependent scattering cross-sections, while the outer echo is redder than predicted. Both dust sheets, if in the foreground, are optically thin for scattering, and the outer echo sheet thickness is consistent with the inferred extinction from peak brightness. Whether the inner echo is from interstellar or circumstellar dust is ambiguous. Overall, the echo characteristics are quite similar to previously observed SN Ia echoes.
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

In this paper, we address the"dual problem"of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene representation. Different from existing single-view methods which can only recover a 2.5D scene representation (i.e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene. Instead of relying on multi-view photo-consistency, our method exploits two information-rich monocular cues, namely shading and shadow, to infer scene geometry. Experiments on multiple challenging datasets show that our method is capable of recovering 3D geometry, including both visible and invisible parts, of a scene from single-view images. Thanks to the neural reflectance field representation, our method is robust to depth discontinuities. It supports applications like novel-view synthesis and relighting. Our code and model can be found at https://ywq.github. io/s3nerf.
What Makes Convolutional Models Great on Long Sequence Modeling?

Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution, making the model unable to handle long-range dependency efficiently. Attention overcomes this problem by aggregating global information but also makes the computational complexity quadratic to the sequence length. Recently, Gu et al. [2021] proposed a model called S4 inspired by the state space model. S4 can be efficiently implemented as a global convolutional model whose kernel size equals the input sequence length. S4 can model much longer sequences than Transformers and achieve significant gains over SoTA on several long-range tasks. Despite its empirical success, S4 is involved. It requires sophisticated parameterization and initialization schemes. As a result, S4 is less intuitive and hard to use. Here we aim to demystify S4 and extract basic principles that contribute to the success of S4 as a global convolutional model. We focus on the structure of the convolution kernel and identify two critical but intuitive principles enjoyed by S4 that are sufficient to make up an effective global convolutional model: 1) The parameterization of the convolutional kernel needs to be efficient in the sense that the number of parameters should scale sub-linearly with sequence length. 2) The kernel needs to satisfy a decaying structure that the weights for convolving with closer neighbors are larger than the more distant ones. Based on the two principles, we propose a simple yet effective convolutional model called Structured Global Convolution (SGConv). SGConv exhibits strong empirical performance over several tasks: 1) With faster speed, SGConv surpasses S4 on Long Range Arena and Speech Command datasets. 2) When plugging SGConv into standard language and vision models, it shows the potential to improve both efficiency and performance.
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction

Knowledge tracing and dropout prediction are crucial for online education to estimate students' knowledge states or to prevent dropout rates. While traditional systems interacting with students suffered from data sparsity and overfitting, recent sample-level contrastive learning helps to alleviate this issue. One major limitation of sample-level approaches is that they regard students' behavior interaction sequences as a bundle, so they often fail to encode temporal contexts and track their dynamic changes, making it hard to find optimal representations for knowledge tracing and dropout prediction. To apply temporal context within the sequence, this study introduces a novel student modeling framework, SAICL: \textbf{s}tudent modeling with \textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive \textbf{l}earning. In detail, SAICL can utilize both proposed self-supervised/supervised interaction-level contrastive objectives: MilCPC (\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised \textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous sample-level contrastive methods for student modeling are highly dependent on data augmentation methods, the SAICL is free of data augmentation while showing better performance in both self-supervised and supervised settings. By combining cross-entropy with contrastive objectives, the proposed SAICL achieved comparable knowledge tracing and dropout prediction performance with other state-of-art models without compromising inference costs.
Label distribution learning via label correlation grid

Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artiﬁcial or environ-mental factors. To alleviate this problem, we propose a L abel C orrelation G rid (LCG) to model the uncertainty of label relationships. Speciﬁcally, we compute a covariance matrix for the label space in the training set to represent the relationships between labels, then model the information distribution (Gaussian distribution function) for each element in the covariance matrix to obtain an LCG. Finally, our network learns the LCG to accurately estimate the label distribution for each instance. In addition, we propose a label distribution projection algorithm as a regularization term in the model training process. Extensive experiments verify the effectiveness of our method on several real benchmarks.
How to Leverage DNN-based speech enhancement for multi-channel speaker verification?

Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise andthe adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-fieldspeaker verification. One approach is a deep neural network-based, and the other is a combination of deep neural network andsignal processing. We integrated a DNN architecture with signal processing techniques to carry out various experiments. Ourapproach is compared to the existing state-of-the-art approaches. We examine the importance of enrollment in pre-processing,which has been largely overlooked in previous studies. Experimental evaluation shows that pre-processing can improve the SVperformance as long as the enrollment files are processed similarly to the test data and that test and enrollment occur within similarSNR ranges. Considerable improvement is obtained on the generated and all the noise conditions of the VOiCES dataset.
A compiler for universal photonic quantum computers

Photons are a natural resource in quantum information, and the last decade showed significant progress in high-quality single photon generation and detection. Furthermore, photonic qubits are easy to manipulate and do not require particularly strongly sealed environments, making them an appealing platform for quantum computing. With the one-way model, the vision of a universal and largescale quantum computer based on photonics becomes feasible. In one-way computing, the input state is not an initial product state $\vert\boldsymbol{0}\rangle^{\otimes n}$, but a socalled cluster state. A series of measurements on the cluster state's individual qubits and their temporal order, together with a feed-forward procedure, determine the quantum circuit to be executed. We propose a pipeline to convert a QASM circuit into a graph representation named measurement-graph (m-graph), that can be directly translated to hardware instructions on an optical one-way quantum computer. In addition, we optimize the graph using ZX-Calculus before evaluating the execution on an experimental discrete variable photonic platform.
Catalogue of solar-like oscillators observed by TESS in 120-second and 20-second cadence

The Transiting Exoplanet Survey Satellite (TESS) mission has provided photometric light curves for stars across nearly the entire sky. This allows for the application of asteroseismology to a pool of potential solar-like oscillators that is unprecedented in size. We aim to produce a catalogue of solar-like oscillators observed by TESS in the 120-second and 20-second cadence modes. The catalogue is intended to highlight stars oscillating at frequencies above the TESS 30-minute cadence Nyquist frequency with the purpose of encompassing the main sequence and subgiant evolutionary phases. We aim to provide estimates for the global asteroseismic parameters $\nu_{\mathrm{max}}$ and $\Delta\nu$. We apply a new probabilistic detection algorithm to the 120-second and 20-second light curves of over 250,000 stars. This algorithm flags targets that show characteristic signatures of solar-like oscillations. We manually vet the resulting list of targets to confirm the presence of solar-like oscillations. Using the probability densities computed by the algorithm, we measure the global asteroseismic parameters $\nu_{\mathrm{max}}$ and $\Delta\nu$. We produce a catalogue of 4,177 solar-like oscillators, reporting $\Delta\nu$ and $\nu_{\mathrm{max}}$ for $98\%$ of the total star count. The asteroseismic data reveals vast coverage of the HR diagram, populating the red giant branch, the subgiant regime and extending toward the main sequence. A crossmatch with external catalogs shows that 25 of the detected solar-like oscillators are a component of a spectroscopic binary, and 28 are confirmed planet host stars. These results provide the potential for precise, independent asteroseismic constraints on these and any additional TESS targets of interest.
LIMIT CYCLE BIFURCATIONS OF A PIECEWISE LINEAR DYNAMICAL SYSTEM

In this paper, we consider a planar dynamical system with a piecewise linear function containing an arbitrary (but finite) number of dropping sections and approximating some continuous nonlinear function. Studying all possible local and global bifurcations of its limit cycles, we give a sketch of the proof of the theorem stating that such a piecewise linear dynamical system with k dropping sections and 2k+1 singular points can have at mostk +2 limit cycles, k +1 of which surround the foci one by one and the last, (k + 2)-th, limit cycle surrounds all of the singular points of this system.
AraLegal-BERT: A pretrained language model for Arabic Legal text

The effectiveness of the bidirectional encoder representations from transformers (BERT) model for multiple linguistic tasks is well documented. However, its potential for a narrow and specific domain, such as legal, has not been fully explored. In this study, we examine the use of BERT in the Arabic legal domain and customize this language model for several downstream tasks using different domain-relevant training and test datasets to train BERT from scratch. We introduce AraLegal-BERT, a bidirectional encoder transformer-based model that has been thoroughly tested and carefully optimized with the goal of amplifying the impact of natural language processing-driven solutions on jurisprudence, legal documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it against three BERT variants for the Arabic language in three natural language understanding tasks. The results showed that the base version of AraLegal-BERT achieved better accuracy than the typical and original BERT model concerning legal texts.
A nonparametric two-sample conditional distribution test

Testing the equality of two conditional distributions is at the core of many modern applications such as domain adaption, transfer learning, and algorithmic fairness. However, to our surprise, little effort has been paid to studying this fundamental problem. In this paper, we contribute to the scarce literature by developing a new two-sample measure named conditional energy distance (CED) to quantify the discrepancy between two conditional distributions without imposing restrictive parametric assumptions. We study the fundamental properties of CED and apply CED to construct a two-sample test for the equality of two conditional distributions. A local bootstrap is developed to approximate the finite sample distribution of the test statistic. The reliable performance of the proposed two-sample conditional distribution test is demonstrated through simulations and a real data analysis.
On the Cosmic Evolution of AGN Obscuration and the X-Ray Luminosity Function: XMM-Newton and Chandra Spectral Analysis of the 31.3 deg2 Stripe 82X

We present X-ray spectral analysis of XMM-Newton and Chandra observations in the 31.3 deg2 Stripe-82X (S82X) field. Of the 6181 unique X-ray sources in this field, we analyze a sample of 2937 candidate active galactic nuclei (AGNs) with solid redshifts and sufficient counts determined by simulations. Our results show an observed population with median values of spectral index Γ=1.94−0.39+0.31 , column density log NH/cm−2=20.7−0.5+1.2 and intrinsic, de-absorbed, 2–10 keV luminosity log LX/ergs−1=44.0−1.0+0.7 , in the redshift range 0–4. We derive the intrinsic, model-independent, fraction of AGNs that are obscured ( 22≤logNH/cm−2<24 ), finding a significant increase in the obscured AGN fraction with redshift and a decline with increasing luminosity. The average obscured AGN fraction is 57% ± 4% for log L X/erg s−1 > 43. This work constrains the AGN obscuration and spectral shape of the still uncertain high-luminosity and high-redshift regimes (log L X/erg s−1 > 45.5, z > 3), where the obscured AGN fraction rises to 64% ± 12%. We report a luminosity and density evolution of the X-ray luminosity function, with obscured AGNs dominating at all luminosities at z > 2, and unobscured sources prevailing at log L X/erg s−1 > 45 at lower redshifts. Our results agree with the evolutionary models in which the bulk of AGN activity is triggered by gas-rich environments and in a downsizing scenario. Moreover, the black hole accretion density (BHAD) is found to evolve similarly to the star formation rate density, confirming the coevolution between AGN and host galaxy, but suggesting different timescales in their growing history. The derived BHAD evolution shows that Compton-thick AGNs contribute to the accretion history of AGNs as much as all other AGN populations combined.
Global city densities: Re-examining urban scaling theory

Understanding scaling relations of social and environmental attributes of urban systems is necessary for effectively managing cities. Urban scaling theory (UST) has assumed that population density scales positively with city size. We present a new global analysis using a publicly available database of 933 cities from 38 countries. Our results showed that (18/38) 47% of countries analyzed supported increasing density scaling (pop ~ area) with exponents ~⅚ as UST predicts. In contrast, 17 of 38 countries (~45%) exhibited density scalings statistically indistinguishable from constant population densities across cities of varying sizes. These results were generally consistent in years spanning four decades from 1975 to 2015. Importantly, density varies by an order of magnitude between regions and countries and decreases in more developed economies. Our results (i) point to how economic and regional differences may affect the scaling of density with city size and (ii) show how understanding country- and region-specific strategies could inform effective management of urban systems for biodiversity, public health, conservation and resiliency from local to global scales. 200 word statement of contribution: Urban Scaling Theory (UST) is a general scaling framework that makes quantitative predictions for how many urban attributes spanning physical, biological and social dimensions scale with city size; thus, UST has great implications in guiding future city developments. A major assumption of UST is that larger cities become denser. We evaluated this assumption using a publicly available global dataset of 933 cities in 38 countries. Our scaling analysis of population size and area of cities revealed that while many countries analyzed showed increasing densities with city size, about 45% of countries showed constant densities across cities. These results question a key assumption of UST. Our results suggest policies and management strategies for biodiversity conservation, public health and sustainability of urban systems may need to be tailored to national and regional scaling relations to be effective.
On the sharp constant in the Bianchi-Egnell stability inequality

This note is concerned with the Bianchi-Egnell inequality, which quan-tiﬁes the stability of the Sobolev inequality, and its generalization to fractional exponents s ∈ (0 , d 2 ) . We prove that in dimension d ≥ 2 the best constant is strictly smaller than the spectral gap constant to sequences which converge to the manifold M of Sobolev optimizers. In particular, c BE ( s ) cannot be asymptotically attained by such sequences. Our proof relies on a precise expansion of the Bianchi-Egnell quotient along a well-chosen sequence of test functions converging to M .
Engineering Equifrequency Contours of Metasurfaces for Self-Collimated Surface Wave Steering

Metasurfaces provide unique capability in guiding surface waves and controlling their polarization and dispersion properties. One way to do that is by analyzing their equifrequency contours. Equifrequency contours are the 2D projection of the 3D dispersion diagram. Since they are a k-space map representation of the surface, many of the wave properties can be understood through the Equifrequency contours. In this paper, we investigate numerically and experimentally the engineering of equifrequency contours using C-shape metasurface design. We show the ability to provide high self-collimation as well as spin-dependent wave splitting for the same metasurface by tuning the frequency of operation. We also show the ability to steer the wave along a deﬁned curved path by rotating the C-shape which results in rotating its equifrequency contours. This work demonstrates how engineering equifrequency contours can be used as a powerful tool for controlling the surface wave propagation properties.