Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining the Size of the Circumgalactic Medium Using the Transverse Autocorrelation Function of C iv Absorbers in Paired Quasar Spectra

The circumgalactic medium (CGM) plays a vital role in the formation and evolution of galaxies, acting as a lifeline between galaxies and the surrounding intergalactic medium. In this study, we leverage a unique sample of quasar pairs to investigate the properties of the CGM with absorption line tomography. We present a new sample of medium-resolution Keck/ESI, Magellan/MagE, and VLT/XSHOOTER spectra of 29 quasar pairs at redshift 2 < z < 3. We supplement the sample with additional spectra of 32 pairs from the literature, creating a catalog of 61 quasar pairs with angular separations between 1. ″7 and 132. ″9 and projected physical separations (r ⊥) between 14 kpc and 887 kpc. We construct a catalog of 906 metal-line absorption doublets of C iv (λλ1548, 1550) with equivalent widths ranging from 6 m Å ≤ W r,1550 ≤ 2053 m Å. The best-fit linear model to the log-space equivalent width frequency distribution ( logf(Wr)=mlog(Wr)+b ) of the sample yields coefficients of m = −1.44 ± 0.16 and b = −0.43 ± 0.16. To constrain the projected extent of C iv, we calculate the transverse autocorrelation function. The flattening of the autocorrelation function at low r ⊥ provides a lower limit for the coherence length of the metal enriched CGM—on the order of 200 h −1 comoving kpc. This physical size constraint allows us to refine our understanding of the metals in the CGM, where the extent of C iv in the CGM depends on gas flows, feedback, timescale of metal injection and mixing, and the mass of the host galaxies.
Boosting the Confidence of Generalization for L2-Stable Randomized Learning Algorithms

Exponential generalization bounds with near-tight rates have recently been established for uniformly stable learning algorithms. The notion of uniform stability, however, is stringent in the sense that it is invariant to the data-generating distribution. Under the weaker and distribution dependent notions of stability such as hypothesis stability and $L_2$-stability, the literature suggests that only polynomial generalization bounds are possible in general cases. The present paper addresses this long standing tension between these two regimes of results and makes progress towards relaxing it inside a classic framework of confidence-boosting. To this end, we first establish an in-expectation first moment generalization error bound for potentially randomized learning algorithms with $L_2$-stability, based on which we then show that a properly designed subbagging process leads to near-tight exponential generalization bounds over the randomness of both data and algorithm. We further substantialize these generic results to stochastic gradient descent (SGD) to derive improved high-probability generalization bounds for convex or non-convex optimization problems with natural time decaying learning rates, which have not been possible to prove with the existing hypothesis stability or uniform stability based results.
Delving into the Pre-training Paradigm of Monocular 3D Object Detection

The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is an efficient way of exploiting the knowledge in unlabeled data. However, the pre-training paradigm for M3OD is hardly studied. We aim to bridge this gap in this work. To this end, we first draw two observations: (1) The guideline of devising pre-training tasks is imitating the representation of the target task. (2) Combining depth estimation and 2D object detection is a promising M3OD pre-training baseline. Afterwards, following the guideline, we propose several strategies to further improve this baseline, which mainly include target guided semi-dense depth estimation, keypoint-aware 2D object detection, and class-level loss adjustment. Combining all the developed techniques, the obtained pre-training framework produces pre-trained backbones that improve M3OD performance significantly on both the KITTI-3D and nuScenes benchmarks. For example, by applying a DLA34 backbone to a naive center-based M3OD detector, the moderate ${\rm AP}_{3D}70$ score of Car on the KITTI-3D testing set is boosted by 18.71\% and the NDS score on the nuScenes validation set is improved by 40.41\% relatively.
Learning in games from a stochastic approximation viewpoint

We develop a uniﬁed stochastic approximation framework for analyzing the long-run behavior of multi-agent online learning in games. Our framework is based on a “primal-dual”, mirrored Robbins–Monro (MRM) template which encompasses a wide array of popular game-theoretic learning algorithms (gradient methods, their optimistic variants, the EXP3 algorithm for learning with payoﬀ-based feedback in ﬁnite games, etc.). In addition to providing an integrated view of these algorithms, the proposed MRM blueprint allows us to obtain a broad range of new convergence results, both asymptotic and in ﬁnite time, in both continuous and ﬁnite games.
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients. However, standardized evaluation and systematical analysis of diverse pFL methods remain a challenge. Firstly, the highly varied datasets, FL simulation settings and pFL implementations prevent easy and fair comparisons of pFL methods. Secondly, the current pFL literature diverges in the adopted evaluation and ablation protocols. Finally, the effectiveness and robustness of pFL methods are under-explored in various practical scenarios, such as the generalization to new clients and the participation of resource-limited clients. To tackle these challenges, we propose the first comprehensive pFL benchmark, pFL-Bench, for facilitating rapid, reproducible, standardized and thorough pFL evaluation. The proposed benchmark contains more than 10 dataset variants in various application domains with a unified data partition and realistic heterogeneous settings; a modularized and easy-to-extend pFL codebase with more than 20 competitive pFL method implementations; and systematic evaluations under containerized environments in terms of generalization, fairness, system overhead, and convergence. We highlight the benefits and potential of state-of-the-art pFL methods and hope the pFL-Bench enables further pFL research and broad applications that would otherwise be difficult owing to the absence of a dedicated benchmark. The code is released at https://github.com/alibaba/FederatedScope/tree/master/benchmark/pFL-Bench.
An Equivariant Tamagawa Number Formula for t-Modules and Applications

We fix motivic data $(K/F, E)$ consisting of a Galois extension $K/F$ of characteristic $p$ global fields with arbitrary abelian Galois group $G$ and an ableian $t$-module $E$, defined over a certain Dedekind subring of $F$. For this data, one can define a $G$-equivariant motivic $L$-function $\Theta_{K/F}^E$. We refine the techniques developed in previous work of the authors and prove an equivariant Tamagawa number formula for appropriate Euler product completions of the special value $\Theta_{K/F}^E(0)$ of this equivariant $L$-function. This extends previous results of the authors from the Drinfeld module setting to the $t$--module setting. As a first notable consequence, we prove a $t$-module analogue of the classical (number field) Refined Brumer-Stark Conjecture, relating a certain $G$-Fitting ideal of the $t$-motive analogue $H(E/K)$ of Taelman's class modules to the special value $\Theta_{K/F}^E(0)$ in question. As a second consequence, we prove formulas for the values $\Theta_{K/F}^E(m)$, at all positive integers $m\in\Bbb Z_{\geq 0}$, when $E$ is a Drinfeld module. This, in turn, implies a Drinfeld module analogue of the classical Refined Coates-Sinnott Conjecture relating $\Theta_{K/F}^E(m)$ to the Fitting ideals of certain Carlitz twists $H(E(m)/\mathcal O_K)$ of Taelman's class modules, suggesting a strong analogy between these twists and the even Quillen $K$-groups of a number field. In an upcoming paper, these consequences will be used to develop an Iwasawa theory for the $t$-module analogues of Taelman's class modules.
Welfare and Distributional Effects of Joint Intervention in Networks

We study a planner’s optimal interventions in both the standalone marginal utilities of players on a network and weights on the links that connect players. The welfare-maximizing joint intervention exhibits the following properties: (a) when the planner’s budget is moderate (so that optimal interventions are interior), the change in weight on any link connecting a pair of players is proportional to the product of eigen-centralities of the pair; (b) when the budget is sufﬁciently large, the optimal network takes a simple form: It is either a complete network under strategic complements or a complete balanced bipartite network under strategic substitutes. We show that the welfare effect of joint intervention is shaped by the principal eigenvalue, while the distributional effect is captured by the dispersion of the corresponding eigenvalues, i.e., the eigen-centralities. Comparing joint intervention in our setting with single intervention solely on the standalone marginal utilities, as studied by Galeotti et al. ( 2020 ), we ﬁnd that joint intervention always yields a higher aggregate welfare, but may lead to greater inequality, which highlights a possible trade-off between the welfare and distributional impacts of joint intervention.
A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction

Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as methods for generation of potential drug candidates. However, a concept of generation of compounds with specific properties and simultaneous modeling of their efficacy against cancer cell lines has not been comprehensively explored. To address this need, we present VADEERS, a Variational Autoencoder-based Drug Efficacy Estimation Recommender System. The generation of compounds is performed by a novel variational autoencoder with a semi-supervised Gaussian Mixture Model (GMM) prior. The prior defines a clustering in the latent space, where the clusters are associated with specific drug properties. In addition, VADEERS is equipped with a cell line autoencoder and a sensitivity prediction network. The model combines data for SMILES string representations of anti-cancer drugs, their inhibition profiles against a panel of protein kinases, cell lines biological features and measurements of the sensitivity of the cell lines to the drugs. The evaluated variants of VADEERS achieve a high r=0.87 Pearson correlation between true and predicted drug sensitivity estimates. We train the GMM prior in such a way that the clusters in the latent space correspond to a pre-computed clustering of the drugs by their inhibitory profiles. We show that the learned latent representations and new generated data points accurately reflect the given clustering. In summary, VADEERS offers a comprehensive model of drugs and cell lines properties and relationships between them, as well as a guided generation of novel compounds.
Neural Collapse: A Review on Modelling Principles and Generalization

Deep classifier neural networks enter the terminal phase of training (TPT) when training error reaches zero and tend to exhibit intriguing Neural Collapse (NC) properties. Neural collapse essentially represents a state at which the within-class variability of final hidden layer outputs is infinitesimally small and their class means form a simplex equiangular tight frame. This simplifies the last layer behaviour to that of a nearest-class center decision rule. Despite the simplicity of this state, the dynamics and implications of reaching it are yet to be fully understood. In this work, we review the principles which aid in modelling neural collapse, followed by the implications of this state on generalization and transfer learning capabilities of neural networks. Finally, we conclude by discussing potential avenues and directions for future research.
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving

Unsupervised contrastive learning for indoor-scene point clouds has achieved great successes. However, unsupervised learning point clouds in outdoor scenes remains challenging because previous methods need to reconstruct the whole scene and capture partial views for the contrastive objective. This is infeasible in outdoor scenes with moving objects, obstacles, and sensors. In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner. CO^3 has several merits compared to existing methods. (1) It utilizes LiDAR point clouds from vehicle-side and infrastructure-side to build views that differ enough but meanwhile maintain common semantic information for contrastive learning, which are more appropriate than views built by previous methods. (2) Alongside the contrastive objective, shape context prediction is proposed as pre-training goal and brings more task-relevant information for unsupervised 3D point cloud representation learning, which are beneficial when transferring the learned representation to downstream detection tasks. (3) As compared to previous methods, representation learned by CO^3 is able to be transferred to different outdoor scene dataset collected by different type of LiDAR sensors. (4) CO^3 improves current state-of-the-art methods on both Once and KITTI datasets by up to 2.58 mAP. Codes and models will be released. We believe CO^3 will facilitate understanding LiDAR point clouds in outdoor scene.
Role of IGF-1 pathway in the pathogenesis of Graves’ orbitopathy

The etiology of Graves’ orbitopathy (GO) remains enigmatic and thus controversy surrounds its pathogenesis. The role of the TSH receptor (TSHR) and the activating antibodies directed against it in the hyperthyroidism of Graves’ disease (GD) is firmly established. Less well elucidated is what part the TSHR pathway might play in the development of GO. Also uncertain is the participation of other cell surface receptors in the disease. Elevated levels of IGF-1 receptor (IGF-1R) have been found in orbital fibroblasts as well as B and T cells from patients with GD. These abnormal patterns of IGF-1R display are also found in rheumatoid arthritis and carry functional consequences. In addition, activating IgGs capable of displacing IGF-1 from IGF-1R have also been detected in patients with these diseases. IGF-1R forms a complex with TSHR which is necessary for at least some of the non-canonical signaling observed following evidence, IGF-1R and TSHR can be thought of as “partners in crime”. Involvement of the former probably transcends disease boundaries, while TSHR may not.
XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions

In the past few years, artificial intelligence (AI) techniques have been implemented in almost all verticals of human life. However, the results generated from the AI models often lag explainability. AI models often appear as a blackbox wherein developers are unable to explain or trace back the reasoning behind a specific decision. Explainable AI (XAI) is a rapid growing field of research which helps to extract information and also visualize the results generated with an optimum transparency. The present study provides and extensive review of the use of XAI in cybersecurity. Cybersecurity enables protection of systems, networks and programs from different types of attacks. The use of XAI has immense potential in predicting such attacks. The paper provides a brief overview on cybersecurity and the various forms of attack. Then the use of traditional AI techniques and its associated challenges are discussed which opens its doors towards use of XAI in various applications. The XAI implementations of various research projects and industry are also presented. Finally, the lessons learnt from these applications are highlighted which act as a guide for future scope of research.
How does overparametrization affect performance on minority groups?

The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known. However, the effect of overparameterization at a more granular level of data subgroups is less understood. Recent empirical studies demonstrate encouraging results: (i) when groups are not known, overparameterized models trained with empirical risk minimization (ERM) perform better on minority groups; (ii) when groups are known, ERM on data subsampled to equalize group sizes yields state-of-the-art worst-group-accuracy in the overparameterized regime. In this paper, we complement these empirical studies with a theoretical investigation of the risk of overparameterized random feature models on minority groups. In a setting in which the regression functions for the majority and minority groups are different, we show that overparameterization always improves minority group performance.
Optimally Band-Limited Noise Filtering for Single-Qubit Gates

We introduce a quantum control protocol that produces smooth, experimentally implementable control sequences optimized to combat temporally correlated noise for single qubit systems. The control ansatz is specif-ically chosen to be a functional expansion of discrete prolate spheroidal sequences, a discrete time basis known to be optimally concentrated in time and frequency, and quite attractive when faced with experimental control hardware constraints. We leverage the ﬁlter function formalism to transform the control problem into a ﬁlter design problem, and show that the frequency response of a quantum system can be carefully tailored to avoid the most relevant dynamical contributions of noise processes. Using gradient ascent, we obtain optimized ﬁlter functions and exploit them to elucidate important details about the relationship between ﬁlter function design, control bandwidth, and noise characteristics. In particular, we identify regimes of optimal noise suppression and in turn, optimal control bandwidth directly proportional to the size of the frequency bands where the noise power is large. In addition to providing guiding principles for ﬁlter design, our approach enables the development of controls that simultaneously yield robust noise ﬁltering and high ﬁdelity single qubit logic operations in a wide variety of complex noise environments.
p-Sparsified Sketches for Fast Multiple Output Kernel Methods

Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations. Sketching, that consists in looking for solutions among a subspace of reduced dimension, is a widely studied approach to alleviate this numerical burden. However, fast sketching strategies, such as non-adaptive subsampling, signiﬁcantly degrade the guarantees of the algorithms, while theoretically-accurate sketches, such as the Gaussian one, turn out to remain relatively slow in practice. In this paper, we introduce the p -sparsiﬁed sketches, that combine the beneﬁts from both approaches to achieve a good tradeoff between statistical accuracy and computational efﬁciency. To support our method, we derive excess risk bounds for both single and multiple output problems, with generic Lipschitz losses, providing new guarantees for a wide range of applications, from robust regression to multiple quantile regression. We also provide empirical evidences of the superiority of our sketches over recent SOTA approaches.
An Improved One millisecond Mobile Backbone

Efﬁcient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efﬁcient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efﬁcient backbone MobileOne , with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efﬁcient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as Mobile-Former while being 38 × faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfﬁcientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks – image classiﬁcation, object detection, and semantic segmentation with signiﬁcant improvements in latency and accuracy as compared to existing efﬁcient architectures when deployed on a mobile device.
Gas Column Density Distribution of Molecular Clouds in the Third Quadrant of the Milky Way

We have obtained column density maps for an unbiased sample of 120 molecular clouds in the third quadrant of the Milky Way midplane (b ≤ ∣5∣°) within the Galactic longitude range from 195° to 225°, using the high-sensitivity 12CO and 13CO (J = 1 − 0) data from the Milky Way Imaging Scroll Painting (MWISP) project. The probability density functions of the molecular hydrogen column density of the clouds, N-pdfs, are fitted with both a lognormal (LN) function and a lognormal plus power-law (LN+PL) function. The molecular clouds are classified into three categories according to their shapes of N-pdfs, i.e., LN, LN+PL, and UN (unclear). About 72% of the molecular clouds fall into the LN category, while 18% and 10% fall into the LN+PL and UN categories, respectively. A PL scaling relation, σs∝NH20.44 , exists between the width of the N-pdf, σ s , and the average column density, NH2 , of the molecular clouds. However, σ s shows no correlation with the mass of the clouds. A correlation is found between the dispersion of normalized column density, σ N/〈N〉, and the sonic Mach number,  , of molecular clouds. Overall, as predicted by numerical simulations, the N-pdfs of the molecular clouds with active star formation activity tend to have N-pdfs with PL high-density tails.
Bayesian additive regression trees for probabilistic programming

Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees' learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPL), i.e., we present BART as a primitive that can be used as a component of a probabilistic model rather than as a standalone model. Specifically, we introduce the Python library PyMC-BART, which works by extending PyMC, a library for probabilistic programming. We showcase a few examples of models that can be built using PyMC-BART, discuss recommendations for the selection of hyperparameters, and finally, we close with limitations of our implementation and future directions for improvement.