Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Multiply Lensed and Binary Quasars in the DESI Legacy Imaging Surveys

The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant (H 0). To achieve H 0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg2 of the extragalactic sky observed in three optical bands (g, r, and z), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ∼5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.
A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation

We introduce P robabilistic R ank and R eward model ( PRR ), a scalable probabilistic model for personalized slate recommendation. Our model allows state-of-the-art estimation of user interests in the following ubiquitous recommender system scenario: A user is shown a ‘slate’ of K recommendations and the user chooses at most one of these K items. It is the goal of the recommender system to ﬁnd the K items of most interest to a user in order to maximize the probability that the user interacts with the slate. Our contribution is to show that we can learn more eﬀectively the probability of the recommendations being successful by combining the reward - whether the slate was clicked or not - and the rank - the item on the slate that was selected. Our method learns more eﬃciently than bandit methods that use only the reward, and user preference methods that use only the rank. It also provides similar or better estimation performance to independent inverse-propensity-score methods and is far more scalable. Our method is state of the art in terms of both speed and accuracy on massive datasets with up to 1 million items. Finally, our method allows fast delivery of recommendations powered by maximum inner product search (MIPS), making it suitable in extremely low latency domains such as computational advertising.
A comparative study of scalable multilevel preconditioners for cardiac mechanics

In this work, we provide a performance comparison between the Balancing Domain Decomposition by Constraints (BDDC) and the Algebraic Multigrid (AMG) preconditioners for cardiac mechanics on both structured and unstructured finite element meshes. The mechanical behavior of myocardium can be described by the equations of three-dimensional finite elasticity, which are discretized by finite elements in space and yield the solution of a large scale nonlinear algebraic system. This problem is solved by a Newton-Krylov method, where the solution of the Jacobian linear system is accelerated by BDDC/AMG preconditioners. We thoroughly explore the main parameters of the BDDC preconditioner in order to make the comparison fair. We focus on: the performance of different direct solvers for the local and coarse problems of the BDDC algorithm; the impact of the different choices of BDDC primal degrees of freedom; and the influence of the finite element degree. Scalability tests are performed on Linux clusters up to 1024 processors, and we conclude with a performance study on a realistic electromechanical simulation.
Multiple Access for Near-Field Communications: SDMA or LDMA?

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.
Unifying local and global model explanations by functional decomposition of low dimensional structures

We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a new identification constraint that allows for the extraction of interventional SHAP values and partial dependence plots, thereby unifying local and global explanations. With our proposed identification, a feature's partial dependence plot corresponds to the main effect term plus the intercept. The interventional SHAP value of feature $k$ is a weighted sum of the main component and all interaction components that include $k$, with the weights given by the reciprocal of the component's dimension. This brings a new perspective to local explanations such as SHAP values which were previously motivated by game theory only. We show that the decomposition can be used to reduce direct and indirect bias by removing all components that include a protected feature. Lastly, we motivate a new measure of feature importance. In principle, our proposed functional decomposition can be applied to any machine learning model, but exact calculation is only feasible for low-dimensional structures or ensembles of those. We provide an algorithm and efficient implementation for gradient-boosted trees (xgboost) and random planted forest. Conducted experiments suggest that our method provides meaningful explanations and reveals interactions of higher orders. The proposed methods are implemented in an R package, available at \url{https://github.com/PlantedML/glex}.
Hyperbolic Molecular Representation Learning for Drug Repositioning

Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.
Slicing4Meta: An Intelligent Integration Framework with Multi-dimensional Network Resources for Metaverse-as-a-Service in Web 3.0

—As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of user-centric immersion and interaction experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential technology to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services ( e.g. , augmented/virtual reality and digital twin) in the three-dimensional virtual world. Nevertheless, there are still no uniﬁed solutions for the Metaverse services in terms of management and orchestration. It is calling for a continuous evolution of scalable solutions and mobile systems to support Metaverse services, and thus bring Metaverse into reality. In this paper, to provide scalable subscription solutions for Metaverse services, we propose a new concept, named Metaverse-as-a-Service (MaaS), in which various physical-virtual components and technologies in the Metaverse can be delivered as services. Furthermore, to unify the management and orchestration of MaaS, we propose a novel framework, called Slicing4Meta, to customize Metaverse services by intelligently integrating MaaS models and the associated multi-dimensional resources on the components and technologies. Additionally, we propose the classiﬁcation for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulﬁll the QoE requirements under the Slicing4Meta framework. We then illustrate a virtual traveling study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges.
Ad Hoc HLA Simulation Data Model Derived From a Model-Based Traffic Scenario

— Modern highly automated and autonomous traffic systems and subsystems require new approaches to test their functional safety in the context of validation and verification. One approach that has taken a leading role in current research is scenario-based testing. For various reasons, simulation is considered to be the most practicable solution for a wide range of test scenarios. However, this is where many existing simulation systems in research reach their limits. In order to be able to integrate the widest possible range of systems to be tested into the simulation, the use of co-simulation has proven to be particularly useful. In this work, the High Level Architecture defined in the IEEE 1516-2010 standard is specifically addressed and a concept is developed that establishes the foundation for the feasible use of scenario-based co-simulation on its basis. The main challenge identified and addressed is the resolution of the double-sided dependency between scenario and simulation models. Finally, the developed concept was implemented as a prototype and the resulting process for its use is presented here using an example scenario. Based on the experience gained during the creation of the concept and the prototype, the next steps for future work are outlined in conclusion.
Developmental Network Two, Its Optimality, and Emergent Turing Machines

OF THE DISCLOSURE This invention includes a new type of neural network that is able to automatically and incrementally generate an internal hierarchy without a need to handcraft a static hierarchy of network areas and a static number of levels and the static number of neurons in each network area or level. This capability is achieved by enabling each neuron to have its own dynamic inhibitory zone using neuron-specific inhibitory connections.
Local Nonlinear Elastic Response of Extracellular Matrices

Nonlinear stiffening is a ubiquitous property of major types of biopolymers that make up the extracellular matrices (ECM) including collagen, fibrin and basement membrane. Within the ECM, many types of cells such as fibroblasts and cancer cells are known to mechanically stretch their surroundings that locally stiffens the matrix. Although the bulk nonlinear elastic behaviors of these biopolymer networks are well studied, their local mechanical responses remain poorly characterized. Here, to understand how a living cell feels the nonlinear mechanical resistance from the ECM, we mimic the cell-applied local force using optical tweezers; we report that the local stiffening responses in highly nonlinear ECM are significantly weaker than responses found in bulk rheology, across two orders of magnitude of the locally applied force since the onset of stiffening. With a minimal model, we show that a local point force application can induce a stiffened region in the matrix, which expands with increasing magnitude of the point force. Furthermore, we show that this stiffened region behaves as an effective probe upon local loading. The local nonlinear elastic response can be attributed to the nonlinear growth of this effective probe that linearly deforms an increasing portion of the matrix.
Low temperature London penetration depth and superfluid density in Fe-based superconductors

The superconducting gap symmetry of the Fe-based superconductors was studied by measurements and analysis of London penetration depth and super uid density. Tunnel diode resonator technique for these measurements was implemented in a dilution refrigerator allowing for the temperatures down to 50 mK. For the analysis of the super uid density, we used both experimental studies of Al-coated samples and original thermodynamic approach based on Rutgers relation. In three systems studied, we found that the superconducting gap at the optimal doping is best described in multi-gap full gap scenario. By performing experiments on samples with arti cially introduced disorder with heavy ion irradiation, we show that evolution of the superconducting transition temperature and of the super uid density are consistent with full-gap sign changing s superconducting state. The superconducting gap develops strong modulation both in the under-doped and the over-doped regimes. In the terminal hole-doped KFe{sub 2}As{sub 2}, both temperature dependence of the super uid density and its evolution with increase of the scattering rate are consistent with symmetry imposed vertical line nodes in the superconducting gap. By comparative studies of hole-doped (Ba,K)Fe{sub 2}As{sub 2} and electron-doped Ca10-3-8, we show that the superconducting gap modulation in the under-doped regime ismore » intrinsic and is not induced by the coexisting static magnetic order. « less
Semi‐Automatic Tuning of Coupled Climate Models With Multiple Intrinsic Timescales: Lessons Learned From the Lorenz96 Model

The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi‐scale dynamics. By considering a toy climate model, namely, the two‐scale Lorenz96 model and producing experiments in perfect‐model setting, we explore in detail how several built‐in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non‐uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research.
Ensembles of realistic power distribution networks

Significance The availability of power distribution network datasets is essential to researchers when testing different control algorithms prior to their actual implementation and deployment on distribution systems. The proprietary nature of actual distribution system data effectively prevents them from being publicly available for use. Most educators and researchers rely on standard test systems published by Institute of Electrical and Electronics Engineers (IEEE), which neither are representative of actual distribution networks nor provide challenges in terms of the size of actual systems. We develop a framework that employs freely available data to construct ensembles of synthetic distribution networks resembling their physical counterparts with realistic scale and complexity. These synthetic networks are geographically embedded and include realistic residential demand profiles, and thus, they can be used to study smart grid applications.
Hamilton principle for chiral anomalies in hydrodynamics

We developed the spacetime-covariant Hamilton principle for barotropic flows of a perfect fluid in the external axial-vector potential conjugate to the helicity current. Such flows carry helicity, a chiral imbalance, controlled by the axial potential. The interest in such a setting is motivated by the recent observation that the axial-current anomaly of quantum field theories with Dirac fermions appears as a kinematic property of classical hydrodynamics. Especially interesting effects occur under the simultaneous actions of the electromagnetic field and the axial-vector potential. With the help of the Hamilton principle, we obtain the extension of the Euler equations by the axial potential and derive anomalies in the divergence of the axial and vector current. Our approach provides a hydrodynamic expression for vector and axial currents and lays down a platform for studying flows with a chiral imbalance and their anomalies.
Towards 6G-V2X: Hybrid RF-VLC for Vehicular Networks

With the advent of connected autonomous vehicles, we are expecting to witness a new era of unprecedented user experiences, improved road safety, a wide range of compelling transportation applications, etc. A large number of disruptive communication technologies are emerging for the sixth generation (6G) wireless network aiming to support advanced use cases for intelligent transportation systems (ITS). An example of such a disruptive technology is constituted by hybrid Visible Light Communication (VLC) and Radio Frequency (RF) systems, which can play a major role in advanced ITS. Hence we outline the potential benefits of hybrid vehicular-VLC (V-VLC) and vehicular-RF (V-RF) communication systems over standalone V-VLC and standalone V-RF systems. In particular, we show that the link-aggregated hybrid V-VLC/V-RF system is capable of meeting stringent ultra high reliability (~99.999%) and ultra-low latency (<3 ms) specifications, making it a promising candidate for 6G ITS. To stimulate future research in the hybrid RF-VLC V2X area, we also highlight the potential challenges and research directions.
A First Course in Linear Model Theory

A Review of Vector and Matrix Algebra. Properties of Special Matrices. Generalized Inverse and Solutions to Linear Systems. The General Linear Model. Multivariate Normal And Related Distributions. Sampling From the Multivariate Normal Distribution. Inference for the General Linear Model. Multiple Regression Models. Fixed Effects Linear Models. Random Effects and Mixed-Effects Models. Special Topics. Appendix.
Real numbers equally compressible in every base

This work solves an open question in finite-state compressibility posed by Lutz and Mayordomo about compressibility of real numbers in different bases. Finite-state compressibility, or equivalently, finite-state dimension, quantifies the asymptotic lower density of information in an infinite sequence. Absolutely normal numbers, being finite-state incompressible in every base of expansion, are precisely those numbers which have finite-state dimension equal to $1$ in every base. At the other extreme, for example, every rational number has finite-state dimension equal to $0$ in every base. Generalizing this, Lutz and Mayordomo (2021) posed the question: are there numbers which have absolute positive finite-state dimension strictly between 0 and 1 - equivalently, is there a real number $\xi$ and a compressibility ratio $s \in (0,1)$ such that for every base $b$, the compressibility ratio of the base-$b$ expansion of $\xi$ is precisely $s$? It is conceivable that there is no such number. Indeed, some works explore ``zero-one'' laws for other feasible dimensions - i.e. sequences with certain properties either have feasible dimension 0 or 1, taking no value strictly in between. However, we answer the question of Lutz and Mayordomo affirmatively by proving a more general result. We show that given any sequence of rational numbers $\langle q_b \rangle$, we can explicitly construct a single number $\xi$ such that for any base $b$, the finite-state dimension/compression ratio of $\xi$ in base-$b$ is $q_b$. As a special case, this result implies the existence of absolutely dimensioned numbers for any given rational dimension between $0$ and $1$, as posed by Lutz and Mayordomo. In our construction, we combine ideas from Wolfgang Schmidt's construction of absolutely normal numbers (1962), results regarding low discrepancy sequences and several new estimates related to exponential sums.
Dual Unet: A Novel Siamese Network for Change Detection with Cascade Differential Fusion

Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights between any pixels between bitemporal images and uses them to engender more discriminating features. In order to improve the feature fusion and avoid gradient vanishing, multi-scale weighted variance map fusion strategy is proposed in the decoding stage. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.