Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection for Classification with QAOA

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real–world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7–qubit IBM (ibm–perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step.
Time averaging for nonautonomous/random linear parabolic equations

Linear nonautonomous/random parabolic partial differential equations are considered under the Dirichlet, Neumann or Robin boundary conditions, where both the zero order coefficients in the equation and the coefficients in the boundary conditions are allowed to depend on time. The theory of the principal spectrum/principal Lyapunov exponents is shown to apply to those equations. In the nonautonomous case, the main result states that the principal eigenvalue of any time-averaged equation is not larger than the supremum of the principal spectrum and that there is a time-averaged equation whose principal eigenvalue is not larger than the infimum of the principal spectrum. In the random case, the main result states that the principal eigenvalue of the time-averaged equation is not larger than the principal Lyapunov exponent.
Interpreting deep learning output for out-of-distribution detection

Commonly used AI networks are very self-confident in their predictions, even when the evidence for a certain decision is dubious. The investigation of a deep learning model output is pivotal for understanding its decision processes and assessing its capabilities and limitations. By analyzing the distributions of raw network output vectors, it can be observed that each class has its own decision boundary and, thus, the same raw output value has different support for different classes. Inspired by this fact, we have developed a new method for out-of-distribution detection. The method offers an explanatory step beyond simple thresholding of the softmax output towards understanding and interpretation of the model learning process and its output. Instead of assigning the class label of the highest logit to each new sample presented to the network, it takes the distributions over all classes into consideration. A probability score interpreter (PSI) is created based on the joint logit values in relation to their respective correct vs wrong class distributions. The PSI suggests whether the sample is likely to belong to a specific class, whether the network is unsure, or whether the sample is likely an outlier or unknown type for the network. The simple PSI has the benefit of being appli-cable on already trained networks. The distributions for correct vs wrong class for each output node are established by simply running the training examples through the trained network. We demonstrate our OOD detection method on a challenging transmission electron microscopy virus image dataset. We simulate a real-world application in which images of virus types unknown to a trained virus classifier, yet acquired with the same procedures and instruments, constitute the OOD samples.
Disentangling Content and Motion for Text-Based Neural Video Manipulation

Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision. Recent advances in deep generative models have led to new approaches that give promising results towards this goal. In this paper, we introduce a new method called DiCoMoGAN for manipulating videos with natural language, aiming to perform local and semantic edits on a video clip to alter the appearances of an object of interest. Our GAN architecture allows for better utilization of multiple observations by disentangling content and motion to enable controllable semantic edits. To this end, we introduce two tightly coupled networks: (i) a representation network for constructing a concise understanding of motion dynamics and temporally invariant content, and (ii) a translation network that exploits the extracted latent content representation to actuate the manipulation according to the target description. Our qualitative and quantitative evaluations demonstrate that DiCoMoGAN significantly outperforms existing frame-based methods, producing temporally coherent and semantically more meaningful results.
3-D Coherent Multitransducer Ultrasound Imaging With Sparse Spiral Arrays

Coherent multitransducer ultrasound (CoMTUS) creates an extended effective aperture through the coherent combination of multiple arrays, which results in images with enhanced resolution, extended field-of-view, and higher sensitivity. The subwavelength localization accuracy of the multiple transducers required to coherently beamform the data is achieved by using the echoes backscattered from targeted points. In this study, CoMTUS is implemented and demonstrated for the first time in 3-D imaging using a pair of 256-element 2-D sparse spiral arrays, which keep the channel count low and limit the amount of data to be processed. The imaging performance of the method was investigated using both simulations and phantom tests. The feasibility of free-hand operation is also experimentally demonstrated. Results show that, in comparison with a single dense array system using the same total number of active elements, the proposed CoMTUS system improves spatial resolution (up to ten times) in the direction where both arrays are aligned, contrast-to-noise ratio (CNR; up to 46%), and generalized CNR (gCNR; up to 15%). Overall, CoMTUS shows a narrower main lobe and higher CNR, which results in an increased dynamic range and better target detectability.
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.
r-PROCESS NUCLEOSYNTHESIS IN DYNAMICALLY EJECTED MATTER OF NEUTRON STAR MERGERS

Although the rapid neutron-capture process, or r-process, is fundamentally important for explaining the origin of approximately half of the stable nuclei with A > 60, the astrophysical site of this process has not been identified yet. Here we study r-process nucleosynthesis in material that is dynamically ejected by tidal and pressure forces during the merging of binary neutron stars (NSs) and within milliseconds afterward. For the first time we make use of relativistic hydrodynamical simulations of such events, defining consistently the conditions that determine the nucleosynthesis, i.e., neutron enrichment, entropy, early density evolution and thus expansion timescale, and ejecta mass. We find that 10−3–10−2 M☉ are ejected, which is enough for mergers to be the main source of heavy (A ≳ 140) galactic r-nuclei for merger rates of some 10−5 yr−1. While asymmetric mergers eject 2–3 times more mass than symmetric ones, the exact amount depends weakly on whether the NSs have radii of ∼15 km for a “stiff” nuclear equation of state (EOS) or ∼12 km for a “soft” EOS. r-process nucleosynthesis during the decompression becomes largely insensitive to the detailed conditions because of efficient fission recycling, producing a composition that closely follows the solar r-abundance distribution for nuclei with mass numbers A > 140. Estimating the light curve powered by the radioactive decay heating of r-process nuclei with an approximative model, we expect high emission in the B-V-R bands for 1–2 days with potentially observable longer duration in the case of asymmetric mergers because of the larger ejecta mass.
New colour–mass-to-light relations: the role of the asymptotic giant branch phase and of interstellar dust

Colour-M/L (mass-to-light) relations are a popular recipe to derive stellar mass in external galaxies. Stellar mass estimates often rely on near infrared (NIR) photometry, considered an optimal tracer since it is little affected by dust and by the "frosting" effect of recent star formation episodes. However, recent literature has highlighted that theoretical estimates of the NIR M/L ratio strongly depend on the modelling of the Asymptotic Giant Branch (AGB) phase. We use the latest Padova isochrones, with detailed modelling of the Thermally Pulsing AGB phase, to update theoretical colour-M/L relations in the optical and NIR and discuss the consequences for the estimated stellar masses in external galaxies. We also discuss the effect of attenuation by interstellar dust on colour-M/L relations in the statistical case of large galaxy samples.
MogaNet: Multi-order Gated Aggregation Network

By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress on \textit{multi-order game-theoretic interaction} within deep neural networks (DNNs) reveals the representation bottleneck of modern ConvNets, where the expressive interactions have not been effectively encoded with the increased kernel size. To tackle this challenge, we propose a new family of modern ConvNets, dubbed MogaNet, for discriminative visual representation learning in pure ConvNet-based models with favorable complexity-performance trade-offs. MogaNet encapsulates conceptually simple yet effective convolutions and gated aggregation into a compact module, where discriminative features are efficiently gathered and contextualized adaptively. MogaNet exhibits great scalability, impressive efficiency of parameters, and competitive performance compared to state-of-the-art ViTs and ConvNets on ImageNet and various downstream vision benchmarks, including COCO object detection, ADE20K semantic segmentation, 2D\&3D human pose estimation, and video prediction. Notably, MogaNet hits 80.0\% and 87.8\% accuracy with 5.2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively. The source code is available at \url{https://github.com/Westlake-AI/MogaNet}.
Permutation representations on Schubert varieties

This paper defines and studies permutation representations on the equivariant cohomology of Schubert varieties, as representations both over ${\Bbb C}$ and over ${\Bbb C}[t_1, t_2,\ldots,t_n]$. We show these group actions are the same as an action of simple transpositions studied geometrically by M. Brion, and give topological meaning to the divided difference operators of Berstein-Gelfand-Gelfand, Demazure, Kostant-Kumar, and others. We analyze these representations using the combinatorial approach to equivariant cohomology introduced by Goresky-Kottwitz-MacPherson. We find that each permutation representation on equivariant cohomology produces a representation on ordinary cohomology that is trivial, though the equivariant representation is not.
Lifting preprojective algebras to orders and categorifying partial flag varieties

We describe a categorification of the cluster algebra structure of multi-homogeneous coordinate rings of partial flag varieties of arbitrary Dynkin type using Cohen-Macaulay modules over orders. This completes the categorification of Geiss-Leclerc-Schroer by adding the missing coefficients. To achieve this, for an order $A$ and an idempotent $e \in A$, we introduce a subcategory $\operatorname{CM}\nolimits_e A$ of $\operatorname{CM}\nolimits A$ and study its properties. In particular, under some mild assumptions, we construct an equivalence of exact categories $(\operatorname{CM}\nolimits_e A)/[Ae] \cong \operatorname{Sub}\nolimits Q$ for an injective $B$-module $Q$ where $B := A/(e)$. These results generalize work by Jensen-King-Su concerning the cluster algebra structure of the Grassmannian $\operatorname{Gr}\nolimits_m(\mathbb{C}^n)$.
NON-ABELIAN COLOR FIELDS FROM RELATIVISTIC COLOR CHARGE CONFIGURATIONS IN THE CLASSICAL LIMIT

We study the dynamics of color fields as generated by simple configurations of relativistic particles with Abelian and non-Abelian (SU(2)) charges in the classical limit. We find that chromodynamic (non-Abelian) systems generally show Coulomb-like features by analogy with electrodynamics. A peculiar feature in the non-Abelian case is the additional strength of the chromoelectric and chromomagnetic fields caused by the contribution of changing the color charge. This change of color SU(2) charges results in a rotation of the color vector which is getting very fast at close partonic distances. The presence of this non-Abelian additional term in the chromoelectric and chromomagnetic fields creates a 'color charge glow', which is manifested as a distinct color wave disturbance arising due to the finite distance at which the color interaction becomes active. This situation may be relevant to the hadronization phase in ultrarelativistic heavy-ion collisions, where the partonic state is governed by strong local color fluctuations.
Resolutions With Conical Slices and Descent for the Brauer Group Classes of Certain Central Reductions of Differential Operators in Characteristic p

“Even more so is the word ‘crystalline’, a glacial and impersonal concept of his which disdains viewing existence from a single portion of time and space” Eileen Myles, “The Importance of Being Iceland” For a smooth variety $X$ over an algebraically closed field of characteristic $p$ to a differential 1-form $\alpha $ on the Frobenius twist $X^{\textrm{(1)}}$ one can associate an Azumaya algebra ${{\mathcal{D}}}_{X,\alpha }$, defined as a certain central reduction of the algebra ${{\mathcal{D}}}_X$ of “crystalline differential operators” on $X$. For a resolution of singularities $\pi :X\to Y$ of an affine variety $Y$, we study for which $\alpha $ the class $[{{\mathcal{D}}}_{X,\alpha }]$ in the Brauer group $\textrm{Br}(X^{\textrm{(1)}})$ descends to $Y^{\textrm{(1)}}$. In the case when $X$ is symplectic, this question is related to Fedosov quantizations in characteristic $p$ and the construction of noncommutative resolutions of $Y$. We prove that the classes $[{{\mathcal{D}}}_{X,\alpha }]$ descend étale locally for all $\alpha $ if ${{\mathcal{O}}}_Y\widetilde{\rightarrow }\pi _\ast{{\mathcal{O}}}_X$ and $R^{1}\pi _*\mathcal O_X = R^2\pi _*\mathcal O_X =0$. We also define a certain class of resolutions, which we call resolutions with conical slices, and prove that for a general reduction of a resolution with conical slices in characteristic $0$ to an algebraically closed field of characteristic $p$ classes $[{{\mathcal{D}}}_{X,\alpha }]$ descend to $Y^{\textrm{(1)}}$ globally for all $\alpha $. Finally we give some examples; in particular, we show that Slodowy slices, Nakajima quiver varieties, and hypertoric varieties are resolutions with conical slices.
CoMaLit-V. Mass forecasting with proxies. Method and application to weak lensing calibrated samples

Mass measurements of astronomical objects are most wanted but still elusive. We need them to trace the formation and evolution of cosmic structure but we can get direct measurements only for a minority. This lack can be circumvented with a proxy and a scaling relation. The twofold goal of estimating the unbiased relation and finding the right proxy value to plug in can be hampered by systematics, selection effects, Eddington/Malmquist biases and time evolution. We present a Bayesian hierarchical method which deals with these issues. Masses to be predicted are treated as missing data in the regression and are estimated together with the scaling parameters. The calibration subsample with measured masses does not need to be representative of the full sample. We apply the method to forecast weak lensing calibrated masses of the Planck, redMaPPer and MCXC clusters. Planck masses are biased low with respect to weak lensing calibrated masses, with a bias more pronounced for high redshift clusters. MCXC masses are under-estimated by ~ 20 per cent, which may be ascribed to hydrostatic bias. Catalogs are made available with the paper.
Free product actions and their applications

A review of our recent works on free product actions, partly in collaborations with Dimitri Shlyakhtenko and with Fumio Hiai, is given. We also work out type III theoretic subfactor analysis on the subfactors constructed in our previous works. This part can be read as supplementary appendix to the works.
The SABRE South experiment at the Stawell Underground Physics Laboratory

The SABRE (Sodium iodide with Active Background REjection) experiment aims to detect an annual rate modulation from dark matter interactions in ultra-high purity NaI(Tl) crystals in order to provide a model independent test of the signal observed by DAMA/LIBRA. It is made up of two separate detectors; SABRE South located at the Stawell Underground Physics Laboratory (SUPL), in regional Victoria, Australia, and SABRE North at the Laboratori Nazionali del Gran Sasso (LNGS). SABRE South is designed to disentangle seasonal or site-related effects from the dark matter-like modulated signal by using an active veto and muon detection system. Ultra-high purity NaI(Tl) crystals are immersed in a linear alkyl benzene (LAB) based liquid scintillator veto, further surrounded by passive steel and polyethylene shielding and a plastic scintillator muon veto. Significant work has been undertaken to understand and mitigate the background processes, that take into account radiation from the detector materials, from both intrinsic and cosmogenic activated processes, and to understand the performance of both the crystal and veto systems. SUPL is a newly built facility located 1024 m underground (about 2900 m water equivalent) within the Stawell Gold Mine and its construction has been completed in mid-2022. The laboratory will house rare event physics searches, including the upcoming SABRE dark matter experiment, as well as measurement facilities to support low background physics experiments and applications such as radiobiology and quantum computing. The SABRE South commissioning is expected to occur in 2023. This paper describes the setup and projections for the experiment, and the description of the underground laboratory.
Orbit portraits in non-autonomous iteration

We extend the definition of an orbit portrait to the context of non-autonomous iteration, both for the combinatorial version involving collections of angles and for the dynamic version involving external rays where combinatorial portraits can be realized by the dynamics associated with sequences of polynomials with suitably uniformly bounded degrees and coefficients. We show that, in the case of sequences of polynomials of constant degree, the portraits which arise are eventually periodic which is somewhat similar to the classical theory of polynomial iteration. However, if the degrees of the polynomials in the sequence are allowed to vary, one can obtain portraits with complementary arcs of irrational length which are fundamentally different from the classical ones.
Another proof of Moon's theorem on generalised tournament score sequences

internal edges, contributing this much to the sum of the scores of the vertices in this subtournament, and possibly some edges leaving from this subtournament to the rest of the tournament. For sufficiency there are numerous proofs, see e.g. [3, 4, 6]. A generalised tournament G = (V (G), α) is a set V (G) = {1, . , n} of vertices along with a function α : V (G) × V (G) → [0, 1], such that α(i, j) + α(j, i) = 1 for all (i, j) ∈ V (G) × V (G), i 6= j, and α(i, i) = 0 for all i ∈ V (G). We will sometimes call the function α the edge–weight function, etc. Note that a tournament is a generalised tournament for which α only takes values in {0, 1}. Given a vertex u ∈ V , the outdegree of u is defined as the sum ∑
Deliberation Networks and How to Train Them

Deliberation networks are a family of sequence-to-sequence models, which have achieved state-of-the-art performance in a wide range of tasks such as machine translation and speech synthesis. A deliberation network consists of multiple standard sequence-to-sequence models, each one conditioned on the initial input and the output of the previous model. During training, there are several key questions: whether to apply Monte Carlo approximation to the gradients or the loss, whether to train the standard models jointly or separately, whether to run an intermediate model in teacher forcing or free running mode, whether to apply task-specific techniques. Previous work on deliberation networks typically explores one or two training options for a specific task. This work introduces a unifying framework, covering various training options, and addresses the above questions. In general, it is simpler to approximate the gradients. When parallel training is essential, separate training should be adopted. Regardless of the task, the intermediate model should be in free running mode. For tasks where the output is continuous, a guided attention loss can be used to prevent degradation into a standard model.