Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-period maser-bearing Miras in the Galactic center. Period-luminosity relations and extinction estimates

We establish a sample of 370 Mira variables that are likely near the Galactic center (GC). The sources have been selected from the OGLE and BAaDE surveys based on their sky coordinates, OGLE classifications, and BAaDE maser-derived line-of-sight velocities. As the distance to the GC is known to a high accuracy, this sample is a test bed for reddening and extinction studies toward the GC and in Mira envelopes. We calculated separate interstellar- and circumstellar-extinction values for individual sources, showing that there is a wide range of circumstellar extinction values (up to four magnitudes in the K$_s$ band) in the sample, and that circumstellar reddening is statistically different from interstellar reddening laws. Further, the reddening laws in the circumstellar environments of our sample and the circumstellar environments of Large Magellanic Cloud (LMC) Miras are strikingly similar despite the different metallicities of the samples. Period-magnitude relations for the mid-infrared (MIR) WISE and MSX bands are also explored, and in the WISE bands we compare these to period-magnitude relationships derived from Miras in the LMC as it is important to compare these LMC relations to those in a higher metallicity environment. Emission from the envelope itself may contaminate MIR magnitudes altering the relations, especially for sources with thick envelopes.
Interleaving GANs with knowledge graphs to support design creativity for book covers

An attractive book cover is important for the success of a book. In this paper, we apply Generative Adversarial Networks (GANs) to the book covers domain, using different methods for training in order to obtain better generated images. We interleave GANs with knowledge graphs to alter the input title to obtain multiple possible options for any given title, which are then used as an augmented input to the generator. Finally, we use the discriminator obtained during the training phase to select the best images generated with new titles. Our method performed better at generating book covers than previous attempts, and the knowledge graph gives better options to the book author or editor compared to using GANs alone.
Comparing scalable strategies for generating numerical perspectives

Numerical perspectives help people understand extreme and unfamiliar numbers (e.g., \$330 billion is about \$1,000 per person in the United States). While research shows perspectives to be helpful, generating them at scale is challenging both because it is difficult to identify what makes some analogies more helpful than others, and because what is most helpful can vary based on the context in which a given number appears. Here we present and compare three policies for large-scale perspective generation: a rule-based approach, a crowdsourced system, and a model that uses Wikipedia data and semantic similarity (via BERT embeddings) to generate context-specific perspectives. We find that the combination of these three approaches dominates any single method, with different approaches excelling in different settings and users displaying heterogeneous preferences across approaches. We conclude by discussing our deployment of perspectives in a widely-used online word processor.
Reconstructing Three-Dimensional Models of Interacting Humans

Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling. However, most of the existing models predict incorrect, lifeless 3d estimates, that miss the subtle human contact aspects--the essence of the event--and are of little use for detailed behavioral understanding. This paper addresses such issues with several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3d contact signature prediction; (2) we show how such components can be leveraged to ensure contact consistency during 3d reconstruction; (3) we construct several large datasets for learning and evaluating 3d contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3d motion capture dataset with 631 sequences containing $2,525$ contact events, $728,664$ ground truth 3d poses, as well as FlickrCI3D, a dataset of $11,216$ images, with $14,081$ processed pairs of people, and $81,233$ facet-level surface correspondences. Finally, (4) we propose methodology for recovering the ground-truth pose and shape of interacting people in a controlled setup and (5) annotate all 3d interaction motions in CHI3D with textual descriptions. Motion data in multiple formats (GHUM and SMPLX parameters, Human3.6m 3d joints) is made available for research purposes at \url{https://ci3d.imar. ro}, together with an evaluation server and a public benchmark.
A Combined Convex Model for Travel Demand Forecasting with Hierarchical Extended Logit Model

The travel demand forecasting model plays a crucial role in evaluating large-scale infrastructure projects, such as the construction of new roads or transit lines. While combined modeling approaches have been explored as a solution to overcome the problem of input and output discrepancies in a sequential four-step modeling process, previous attempts at combined models have encountered challenges in real-world applications, primarily due to their limited behavioral richness or computational tractability. In this study, we propose a novel convex programming approach and present a key theorem demonstrating that the optimal solution is the same as the one from solving the hierarchical extended logit model. This model is specifically designed to capture correlations existing in travelers' choices, including similarities among transport modes and route overlaps. The convex property of our model ensures the existence and uniqueness of solutions and offers computational efficiency. The advantages of our proposed model are twofold. Firstly, it provides a single unifying rationale (i.e., utility maximization) that is valid across all steps. Secondly, its combined nature allows a systematic approach to handling observed data, enabling a more realistic representation of reality. By addressing the limited behavior richness of previous combined models, our convex programming-based approach shows promise in enhancing the accuracy and applicability of travel demand forecasting, thereby aiding in the planning and decision-making processes for infrastructure projects.
Meta-heuristics for Multidimensional Knapsack Problems

In this research, we present the use of genetic algorithms and constraint handling techniques used in genetic algorithms to solve Multidimensional Knapsack Problems (MKP). We also investigate and compare how the use of different genetic algorithm operators and parameters could obtain different solutions for the MKP. Three proposed genetic algorithms (basic GA, GA with penalty function1, and GA with penalty function2) are implemented. The performance of these genetic algorithms is compared based on some small MKP. Finally, a well-known MKP is modified as a fictitious capital budgeting problem, and the results are reported. The experiment results show that genetic algorithms are capable to solve MKP and capital budgeting problems.
Fundamental data structures for matrix-free finite elements on hybrid tetrahedral grids

This paper presents efficient data structures for the implementation of matrix-free finite element methods on block-structured, hybrid tetrahedral grids. It provides a complete categorization of all geometric sub-objects that emerge from the regular refinement of the unstructured, tetrahedral coarse grid and describes efficient iteration patterns and analytical linearization functions for the mapping of coefficients to memory addresses. This foundation enables the implementation of fast, extreme-scalable, matrix-free, iterative solvers, and in particular geometric multigrid methods by design. Their application to the variable-coefficient Stokes system subject to an enriched Galerkin discretization and to the curl-curl problem discretized with Nédélec edge elements showcases the flexibility of the implementation. Finally, the solution of a curl-curl problem with $ 1.6\cdot 10^{11} $ 1.6⋅1011 (more than one hundred billion) unknowns on more than 32,000 processes with a matrix-free full multigrid solver demonstrates its extreme-scalability. GRAPHICAL ABSTRACT
Towards a Safe Real-Time Motion Planning Framework for Autonomous Driving Systems: An MPPI Approach

Planning safe trajectories in Autonomous Driving Systems (ADS) is a complex problem to solve in real-time. The main challenge to solve this problem arises from the various conditions and constraints imposed by road geometry, semantics and traffic rules, as well as the presence of dynamic agents. Recently, Model Predictive Path Integral (MPPI) has shown to be an effective framework for optimal motion planning and control in robot navigation in unstructured and highly uncertain environments. In this paper, we formulate the motion planning problem in ADS as a nonlinear stochastic dynamic optimization problem that can be solved using an MPPI strategy. The main technical contribution of this work is a method to handle obstacles within the MPPI formulation safely. In this method, obstacles are approximated by circles that can be easily integrated into the MPPI cost formulation while considering safety margins. The proposed MPPI framework has been efficiently implemented in our autonomous vehicle and experimentally validated using three different primitive scenarios. Experimental results show that generated trajectories are safe, feasible and perfectly achieve the planning objective. The video results as well as the open-source implementation are available at: https://gitlab.uni. lu/360lab-public/mppi
Optimal Online Discrepancy Minimization

We prove that there exists an online algorithm that for any sequence of vectors $v_1,\ldots,v_T \in \mathbb{R}^n$ with $\|v_i\|_2 \leq 1$, arriving one at a time, decides random signs $x_1,\ldots,x_T \in \{ -1,1\}$ so that for every $t \le T$, the prefix sum $\sum_{i=1}^t x_iv_i$ is $10$-subgaussian. This improves over the work of Alweiss, Liu and Sawhney who kept prefix sums $O(\sqrt{\log (nT)})$-subgaussian, and gives a $O(\sqrt{\log T})$ bound on the discrepancy $\max_{t \in T} \|\sum_{i=1}^t x_i v_i\|_\infty$. Our proof combines a generalization of Banaszczyk's prefix balancing result to trees with a cloning argument to find distributions rather than single colorings. We also show a matching $\Omega(\sqrt{\log T})$ strategy for an oblivious adversary.
Dynamic responses of anchored ducted flames to harmonic velocity forcing

This paper aims at developing a computationally inexpensive method to investigate the premixed flame instabilities. The kinematic G-equation is combined with a two-dimensional discrete vortex method, and the conformal mapping is applied to make calculations for complicated geometries more efficiently. The vortex dynamics and flame response to harmonic velocity forcing of an anchored ducted V-flame are investigated, and the effects of harmonic forcing, Reynolds number, and bluff body geometry are examined. Results show that the vortex structures, flow instability, and flame response are closely coupled with each other. The unsteady vortex structures generate instabilities at the flame base, and the convection of the flame wrinkles then influences the flame dynamics downstream. The flame heat release fluctuates with larger amplitude under low-frequency forcings, while the phase of the flame transfer function is quasi-linear with increasing forcing frequency. Both higher inflow velocity and sharper bluff body corners can result in more unsteady large-scale vortex structures and hence influence the flame responses.
Similar Image Retrieval using Autoencoder. I. Automatic Morphology Classification of Galaxies

We present the construction of an image similarity retrieval engine for the morphological classification of galaxies using the Convolutional AutoEncoder (CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey galaxy images listed in the Galaxy Zoo 2 (GZ2) catalog. The visually similar output images returned by the trained CAE suggest that the encoder efficiently compresses input images into latent features, which are then used to calculate similarity parameters. Our Tool for Searching a similar Galaxy Image based on a Convolutional Autoencoder using Similarity (TSGICAS) leverages this similarity parameter to classify galaxies’ morphological types, enabling the identification of a wider range of classes with high accuracy compared to traditional supervised ML techniques. This approach streamlines the researcher’s work by allowing quick prioritization of the most relevant images from the latent feature database. We investigate the accuracy of our automatic morphological classifications using three galaxy catalogs: GZ2, Extraction de Formes Idéalisées de Galaxies en Imagerie (EFIGI), and Nair & Abraham (NA10). The correlation coefficients between the morphological types of input and retrieved galaxy images were found to be 0.735, 0.811, and 0.815 for GZ2, EFIGI, and NA10 catalogs, respectively. Despite differences in morphology tags between input and retrieved galaxy images, visual inspection showed that the two galaxies were very similar, highlighting TSGICAS’s superior performance in image similarity search. We propose that morphological classifications of galaxies using TSGICAS are fast and efficient, making it a valuable tool for detailed galaxy morphological classifications in other imaging surveys.
Data-based approach for time-correlated closures of turbulence models.

Developed turbulent motion of fluid still lacks an analytical description despite more than a century of active research. Nowadays, phenomenological ideas are widely used in practical applications, such as small-scale closures for numerical simulations of turbulent flows. In the present paper, we use a shell model of turbulence to construct a closure intended to have a solid theoretical background and to capture intrinsic probabilistic features of turbulence. Shell models of turbulence are dynamical deterministic systems used to model energy cascade and other key aspects of the Navier-Stokes such as intermittency. We rescale the variables of the Sabra model in a way which leads to hidden symmetries and universal distributions. We then use such fine distributions to write closures, i.e., missing expressions for some of the Sabra variables. Our closures rely on approximating probability density functions using a Gaussian mixture model, which makes them probabilistic by nature and allows us to write time-correlated closures. We also provide a framework where other machine learning tools can be employed with reduced black-box aspects.
Tight Bounds for Local Glivenko-Cantelli

This paper addresses the statistical problem of estimating the infinite-norm deviation from the empirical mean to the distribution mean for high-dimensional distributions on $\{0,1\}^d$, potentially with $d=\infty$. Unlike traditional bounds as in the classical Glivenko-Cantelli theorem, we explore the instance-dependent convergence behavior. For product distributions, we provide the exact non-asymptotic behavior of the expected maximum deviation, revealing various regimes of decay. In particular, these tight bounds demonstrate the necessity of a previously proposed factor for an upper bound, answering a corresponding COLT 2023 open problem. We also consider general distributions on $\{0,1\}^d$ and provide the tightest possible bounds for the maximum deviation of the empirical mean given only the mean statistic. Along the way, we prove a localized version of the Dvoretzky-Kiefer-Wolfowitz inequality. Additionally, we present some results for two other cases, one where the deviation is measured in some $q$-norm, and the other where the distribution is supported on a continuous domain $[0,1]^d$, and also provide some high-probability bounds for the maximum deviation in the independent Bernoulli case.
Spontaneous imbibition and solvent diffusion in fractured porous media by LBM

Enhanced oil recovery (EOR) from naturally fractured reservoirs (NFR’s) involves the injection of external agents into the fracture networks to extract the resident crude oil in the matrix. Characteristically, these agents displace the oil by diffusion (if miscible) or spontaneous imbibition (if immiscible). The present work performs two-dimensional simulations of these processes at the pore-scale and extends the results to core-scale by using the Lattice Boltzmann method (LBM). Simulations of miscible recovery of kerosene and light mineral oil by injection of pentane are performed to observe the displacement profiles. The LBM model is then extended to simulate the co-current and counter-current spontaneous imbibition of water in a kerosene saturated water-wet sand pack. The oil-water interface development is analyzed at the pore-scale, and the saturation profiles are compared with experiments from literature. Two-phase relative permeabilities of the displacement processes are determined, and their dependence on various physical parameters is studied.
Cross-phase modulation in the two dimensional spectroscopy

Developing from the transient absorption (TA) spectroscopy, the two dimensional (2D) spectroscopy with pump-probe geometry has emerged as a versatile approach for alleviating the difficulty on implementing the 2D spectroscopy with other geometries. However, the presence of cross-phase modulation (XPM) in TA spectroscopy introduces significant spectral distortions, particularly when the pump and probe pulses overlap. We demonstrate that this phenomenon is extended to the 2D spectroscopy with pump-probe geometry and the XPM is induced by the interference of the two pump pulse. We present the oscillatory behavior of XPM in the 2D spectrum and its displacement with respect to the waiting time delay through both experimental measurements and numerical simulations. Additionally, we explore the influence of probe pulse chirp on XPM and discover that by compressing the chirp, the impact of XPM on the desired signal can be reduced.
Learning Prediction Intervals for Regression: Generalization and Calibration: Supplementary Materials

We provide further results and discussions in this supplemental material. Appendix A presents results on the consistency of the obtained PI from the empirical constrained optimization. Appendix B provides additional discussion on Theorem 4.3. Appendix C shows the joint coverage-width guarantee for the linear hypothesis class. Appendix D shows an alternate analysis for neural networks using Pollard’s pseudo-dimension and our derived results for the VC-subgraph class. Appendix E further discusses the finite-sample guarantees for our coverage calibration procedure. Appendix F presents and explains an alternate calibration procedure. Appendix G discusses a Lagrangian formulation to train neural networks that construct PIs. Appendix H reviews some background in empirical processes. Appendix I illustrates experimental details and additional experimental results. Finally, Appendix J shows all technical proofs.
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, especially in fluorescence microscopy. Generative networks such as CycleGAN can inverse the process and generate synthetic microscopy images for a given mask, thereby building a synthetic dataset. However, past works report content inconsistencies between the mask and generated image, partially due to CycleGAN minimizing its loss by hiding shortcut information for the image reconstruction in high frequencies rather than encoding the desired image content and learning the target task. In this work, we propose to remove the hidden shortcut information, called steganography, from generated images by employing a low pass filtering based on the discrete cosine transform (DCT). We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task. Here we achieve an improvement of 5.4 percentage points in the F1-score compared to a vanilla CycleGAN. Integrating advanced regularization techniques into the CycleGAN architecture may help mitigate steganography-related issues and produce more accurate synthetic datasets for nuclei segmentation.
Bicategorical Models of Classical Propositional Logic

F\"uhrmann and Pym constructed models of classical propositional logic in an order-enriched categorical setting, whose typical example is the category $\mathbf{Rel}$ of sets and relations. It is remarkable in that they are both non-degenerate and symmetric, i.e., free from the choices of the reduction strategy. As a furter categorification of this direction, we give bicategorical models of classical propositional logic that is also symmetric and non-degenerate. Primal examples of our models include $\mathbf{Rel}$, $\mathbf{Span}$, and $\mathbf{Prof}$, which shows that we can construct models that are non-degenerate not only for $1$-cells but also for $2$-cells and the logical negations.
A random graph growth model

A growing random graph is constructed by successively sampling without replacement an element from the pool of virtual vertices and edges. At start of the process, the pool contains virtual vertices and no edges. Each time a vertex is sampled and occupied, the edges linking the vertex to previously occupied vertices are added to the pool of virtual elements. We focus on the edge‐counting at times when the graph has occupied vertices. Two different Poisson limits are identified for and . For the bulk of the process, when , the scaled number of edges is shown to fluctuate about a deterministic curve, with fluctuations being of the order of and approximable by a Gaussian bridge.
A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on the image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.
Coarse-Grained Simulations Of Dna And Rna Systems With Oxdna And Oxrna Models: Tutorial

We present a tutorial on setting-up the oxDNA coarse-grained model for simulations of DNA and RNA nanotechnology. The model is a popular tool used both by theorists and experimentalists to simulate nucleic acid systems both in biology and nanotechnology settings. The tutorial is aimed at new users asking "Where should I start if I want to use oxDNA". We assume no prior background in using the model. This tutorial shows basic examples that can get a novice user started with the model, and points the prospective user towards additional reading and online resources depending on which aspect of the model they are interested in pursuing.