Miracle-dz/UniFAR-FTUs huggingface dataset:
A twelve-quadrupole correction for the interaction regions of high-energy accelerators.

Corrections of gradient errors in the interactions regions (IRs) of high energy colliders have traditionally been made by changing the strengths of quadrupoles that are common to both beams, such as the triplet quadrupoles. This article shows that magnetic errors in the IR quadrupoles that are no common to both beams, such as the matching quadrupoles, can have an important influence and, therefore, the correction should also include these quadrupoles. A correction based on twelve IR quadrupoles (common and no common) is presented and validated through MADX simulations. To estimate the strengths of this correction, the action and phase in the inter-triplet space, the space that separates the two triplets of the IR, are required. A novel method to estimate these quantities is also presented. The main sources of uncertainties in this novel method are identified and compared to the current method that uses two beam position monitor within the inter-triplet space. Finally, LHC experimental data is used to estimate the strengths of a twelve-quadrupole correction in the interaction region 1 of the LHC. The resulting correction is compared with a six-quadrupole correction estimated with another method called segment-by-segment (SBS).
Coordinated Passive Beamforming for Distributed Intelligent Reflecting Surfaces Network

Intelligent reflecting surface (IRS) is a proposing technology in 6G to enhance the performance of wireless networks by smartly reconfiguring the propagation environment with a large number of passive reflecting elements. However, current works mainly focus on single IRS-empowered wireless networks, where the channel rank deficiency problem has emerged. In this paper, we propose a distributed IRS-empowered communication network architecture, where multiple source-destination pairs communicate through multiple distributed IRSs. We further contribute to maximize the achievable sum-rates in this network via jointly optimizing the transmit power vector at the sources and the phase shift matrix with passive beamforming at all distributed IRSs. Unfortunately, this problem turns out to be non-convex and highly intractable, for which an alternating approach is developed via solving the resulting fractional programming problems alternatively. In particular, the closed-form expressions are proposed for coordinated passive beamforming at IRSs. The numerical results will demonstrate the algorithmic advantages and desirable performances of the distributed IRS-empowered communication network.
Inference of Solar Rotation from Perturbations of Acoustic Mode Eigenfunctions

Today’s picture of the internal solar rotation rate profile results essentially from helioseismic analyses of frequency splittings of resonant acoustic waves. Here we present another, complementary estimation of the internal solar rotation rate using the perturbation of the shape of the acoustic waves. For this purpose, we extend a global helioseismic approach developed previously for the investigation of the meridional flow to work on the components of the differential rotation. We discuss the effect of rotation on mode eigenfunctions and the observables based thereon. Based on a numerical study using a simulated rotation rate profile, we tailor an inversion approach and also consider the case of the presence of an additional meridional flow. This inversion approach is then applied to data from the Michelson Doppler Imager (MDI) on board the Solar Heliospheric Observatory and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. In the end, rotation rate profiles estimated from eigenfunction perturbation and frequency splittings are compared. The rotation rate profiles from the two different approaches are qualitatively in good agreement, especially for the MDI data. Significant differences are obtained at high latitudes >50° and near the subsurface. The result from HMI data shows larger discrepancies between the different methods. We find that the two global helioseismic approaches provide complementary methods for measuring the solar rotation. Comparing the results from different methods may help to reveal systematic influences that affect analyses based on eigenfunction perturbations, like meridional flow measurements.
AnyHLS: High-Level Synthesis With Partial Evaluation

Field programmable gate arrays (FPGAs) excel in low power and high throughput computations, but they are challenging to program. Traditionally, developers rely on hardware description languages, such as Verilog or VHDL to specify the hardware behavior at the register-transfer level. High-level synthesis (HLS) raises the level of abstraction but still requires FPGA design knowledge. Programmers usually write pragma-annotated C/C++ programs to define the hardware architecture of an application. However, each hardware vendor extends its own C dialect using its own vendor-specific set of pragmas. This prevents portability across different vendors. Furthermore, pragmas are not first-class citizens in the language. This makes it hard to use them in a modular way or design proper abstractions. In this article, we present AnyHLS, an approach to synthesize FPGA designs in a modular and abstract way. AnyHLS is able to raise the abstraction level of the existing HLS tools by resorting to programming language features such as types and higher order functions as follows. It relies on partial evaluation to specialize and to optimize the user application based on a library of abstractions. Then, vendor-specific HLS code is generated for Intel and Xilinx FPGAs. Portability is obtained by avoiding any vendor-specific pragmas at the source code. In order to validate achievable gains in productivity, a library for the domain of image processing is introduced as a case study, and its synthesis results are compared with several state-of-the-art domain-specific language (DSL) approaches for this domain.
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\wedge$) and existential quantifiers ($\exists$). Handling queries with logical disjunctions ($\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\wedge$, $\vee$, and $\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\wedge$, $\vee$, $\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.
Stable recovery of a non-compactly supported coefficient of a Schrödinger equation on an infinite waveguide

We study the stability issue for the inverse problem of determining a coefficient appearing in a Schrödinger equation defined on an infinite cylindrical waveguide. More precisely, we prove the stable recovery of some general class of non-compactly and non periodic coefficients appearing in an unbounded cylindrical domain. We consider both results of stability from full and partial boundary measurements associated with the so called Dirichlet-to-Neumann map.
Local continuity of log-concave projection, with applications to estimation under model misspecification

The log-concave projection is an operator that maps a d-dimensional distribution P to an approximating log-concave density. Prior work by D{u}mbgen et al. (2011) establishes that, with suitable metrics on the underlying spaces, this projection is continuous, but not uniformly continuous. In this work we prove a local uniform continuity result for log-concave projection -- in particular, establishing that this map is locally H{o}lder-(1/4) continuous. A matching lower bound verifies that this exponent cannot be improved. We also examine the implications of this continuity result for the empirical setting -- given a sample drawn from a distribution P, we bound the squared Hellinger distance between the log-concave projection of the empirical distribution of the sample, and the log-concave projection of P. In particular, this yields interesting statistical results for the misspecified setting, where P is not itself log-concave.
Is there an early Universe solution to Hubble tension?

We consider a low redshift $(z<0.7)$ cosmological dataset comprising megamasers, cosmic chronometers, type Ia SNe and BAO, which we bin according to their redshift. For each bin, we read the value of $H_0$ by fitting directly to the flat $\Lambda$CDM model. Doing so, we find that $H_0$ descends with redshift, allowing one to fit a line with a \textit{non-zero} slope of statistical significance $2.1 \, \sigma$. Our results are in accord with a similar descending trend reported by the H0LiCOW collaboration. If substantiated going forward, early Universe solutions to the Hubble tension will struggle explaining this trend.
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still in its infancy. The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. To achieve this goal, we propose a synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which effectively encodes the spatio-temporal information of asynchronous retinal events into TSLTD frames with clear motion patterns. We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression. Our method is compared with state-of-the-art object tracking methods, that are based on conventional cameras or event cameras. The experimental results show the superiority of our method in handling various challenging environments such as fast motion and low illumination conditions.
Adversarial Distributional Training for Robust Deep Learning

Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples. However, the adversarially trained models do not perform well enough on test data or under other attack algorithms unseen during training, which remains to be improved. In this paper, we introduce a novel adversarial distributional training (ADT) framework for learning robust models. Specifically, we formulate ADT as a minimax optimization problem, where the inner maximization aims to learn an adversarial distribution to characterize the potential adversarial examples around a natural one, and the outer minimization aims to train robust classifiers by minimizing the expected loss over the worst-case adversarial distributions. We conduct a theoretical analysis on how to solve the minimax problem, leading to a general algorithm for ADT. We further propose three different approaches to parameterize the adversarial distributions. Empirical results on various benchmarks validate the effectiveness of ADT compared with the state-of-the-art AT methods.
Understanding the physical properties of young M dwarfs: NIR spectroscopic studies

We present here medium resolution (\lambda /\Delta\lambda\sim1200) H- and K-band spectra of M type dwarf stars covering the wavelength range 1.50 - 1.80 {\mu}m and 1.95 - 2.45 {\mu}m. The sample includes 53 dwarf stars (M0V-M7V) from new observations using the TIFR Near-Infrared Spectrometer and Imager (TIRSPEC) instrument on the 2-m Himalayan Chandra Telescope (HCT). Using interferometrically-measured effective temperature (T_{eff}), radius and luminosity of nearby bright calibrator stars, we have created new empirical relationships among those fundamental parameters and spectral indices. The equivalent widths of H-band spectral features like Mg (1.57 {\mu}m), Al (1.67 {\mu}m) and Mg (1.71 {\mu}m), and the H_{2}O-H index are found to be good indicators of T_{eff}, radius and luminosity and we establish the linear functions using these features relating to those stellar parameters. The root mean squared error (RMSE) of our best fits are 102K, 0. 027R_{\sun} and 0.12dex respectively. Using spectral type standards along with known parallaxes, we calibrate both H and K-band H_{2}O indices as a tracer of spectral type and absolute K_{s} magnitude. Metallicities of M-dwarf samples are estimated using the K-band calibration relationships. The mass of M-dwarfs could be determined using the luminosity (L/L_{\sun}) and we establish a new empirical relation for this. We also compare and contrast our results with other similar work from the literature.
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset

The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers’ proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo’s self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. In addition, a visualization tool is presented for verifying the performance of the model.
Multiple Metric Learning for Structured Data

We address the problem of merging graph and feature-space information while learning a metric from structured data. Existing algorithms tackle the problem in an asymmetric way, by either extracting vectorized summaries of the graph structure or adding hard constraints to feature-space algorithms. Following a different path, we define a metric regression scheme where we train metric-constrained linear combinations of dissimilarity matrices. The idea is that the input matrices can be pre-computed dissimilarity measures obtained from any kind of available data (e.g. node attributes or edge structure). As the model inputs are distance measures, we do not need to assume the existence of any underlying feature space. Main challenge is that metric constraints (especially positive-definiteness and sub-additivity), are not automatically respected if, for example, the coefficients of the linear combination are allowed to be negative. Both positive and sub-additive constraints are linear inequalities, but the computational complexity of imposing them scales as O(D3), where D is the size of the input matrices (i.e. the size of the data set). This becomes quickly prohibitive, even when D is relatively small. We propose a new graph-based technique for optimizing under such constraints and show that, in some cases, our approach may reduce the original computational complexity of the optimization process by one order of magnitude. Contrarily to existing methods, our scheme applies to any (possibly non-convex) metric-constrained objective function.
Evidence for Spin–Orbit Alignment in the TRAPPIST-1 System

In an effort to measure the Rossiter–McLaughlin effect for the TRAPPIST-1 system, we performed high-resolution spectroscopy during transits of planets e, f, and b. The spectra were obtained with the InfraRed Doppler spectrograph on the Subaru 8.2 m telescope, and were supplemented with simultaneous photometry obtained with a 1 m telescope of the Las Cumbres Observatory Global Telescope. By analyzing the anomalous radial velocities, we found the projected stellar obliquity to be λ = 1 ± 28° under the assumption that the three planets have coplanar orbits, although we caution that the radial-velocity data show correlated noise of unknown origin. We also sought evidence for the expected deformations of the stellar absorption lines, and thereby detected the “Doppler shadow” of planet b with a false-alarm probability of 1.7%. The joint analysis of the observed residual cross-correlation map including the three transits gave °. These results indicate that the the TRAPPIST-1 star is not strongly misaligned with the common orbital plane of the planets, although further observations are encouraged to verify this conclusion.
Transport Properties in Magnetized Compact Stars

Transport properties of dense quark matter are discussed in the strong magnetic field, B. B dependence as well as density dependence of the Hall conductivity is discussed, based on the microscopic Kubo formula. We took into account the possibility of the inhomogeneous chiral phase at moderate densities, where anomalous Hall effect is intrinsic and resembles the one in Weyl semimetals in condensed matter physics. Some theoretical aspects inherent in anomalous Hall effect are also discussed.
Optimal estimation of high-dimensional Gaussian mixtures

This paper studies the optimal rate of estimation in a finite Gaussian location mixture model in high dimensions without separation conditions. We assume that the number of components $k$ is bounded and that the centers lie in a ball of bounded radius, while allowing the dimension $d$ to be as large as the sample size $n$. Extending the one-dimensional result of Heinrich and Kahn \cite{HK2015}, we show that the minimax rate of estimating the mixing distribution in Wasserstein distance is $\Theta((d/n)^{1/4} + n^{-1/(4k-2)})$, achieved by an estimator computable in time $O(nd^2+n^{5/4})$. Furthermore, we show that the mixture density can be estimated at the optimal parametric rate $\Theta(\sqrt{d/n})$ in Hellinger distance; however, no computationally efficient algorithm is known to achieve the optimal rate. Both the theoretical and methodological development rely on a careful application of the method of moments. Central to our results is the observation that the information geometry of finite Gaussian mixtures is characterized by the moment tensors of the mixing distribution, whose low-rank structure can be exploited to obtain a sharp local entropy bound.
Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data

The continuous expansion of the urban traffic sensing infrastructure has led to a surge in the volume of widely available road related data. Consequently, increasing effort is being dedicated to the creation of intelligent transportation systems, where decisions on issues ranging from city-wide road maintenance planning to improving the commuting experience are informed by computational models of urban traffic instead of being left entirely to humans. The automation of traffic management has received substantial attention from the research community, however, most approaches target highways, produce predictions valid for a limited time window or require expensive retraining of available models in order to accurately forecast traffic at a new location. In this article, we propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways. Additionally, there is no need to retrain the model for a period of up to 9 weeks. Furthermore, the proposed method generates models that are transferable to other segments of the road network, similar to, yet geographically distinct from the ones they were initially trained on. We demonstrate the achievement of these claims by conducting extensive experiments on data collected from the Darmstadt urban infrastructure.
Riemann-Hilbert correspondence for differential systems over Riemann surfaces

Let $G$ be a connected reductive affine algebraic group defined over $\mathbb C$ and $\mathfrak g$ its Lie algebra. We study the monodromy map from the space of $\mathfrak g$-differential systems on a compact connected Riemann surface $\Sigma$ of genus $g \,\geq\, 2$ to the character variety of $G$-representations of the fundamental group of $\Sigma$. If the complex dimension of $G$ is at least three, we show that the monodromy map is an immersion at the generic point.
xGASS: passive discs do not host unexpectedly large reservoirs of cold atomic hydrogen

We use the extended GALEX Arecibo SDSS Survey (xGASS) to quantify the relationship between atomic hydrogen (HI) reservoir and current star formation rate (SFR) for central disk galaxies. This is primarily motivated by recent claims for the existence, in this sample, of a large population of passive disks harbouring HI reservoirs as large as those observed in main sequence galaxies. Across the stellar mass range 10$^{9}<$M$_{*}$/M$_{\odot}<$10$^{11}$, we practically find no passive ($\gtrsim$2$\sigma$ below the star-forming main sequence) disk galaxies with HI reservoirs comparable to those typical of star-forming systems. Even including HI non detections at their upper limits, passive disks typically have $\geq$0.5 dex less HI than their active counterparts. We show that previous claims are due to the use of aperture-corrected SFR estimates from the MPA/JHU SDSS DR7 catalog, which do not provide a fair representation of the global SFR of HI-rich galaxies with extended star-forming disks. Our findings confirm that the bulk of the passive disk population in the local Universe is HI-poor. These also imply that the reduction of star formation, even in central disk galaxies, has to be accompanied by a reduction in their HI reservoir.
Eliminating the wave-function singularity for ultracold atoms by a similarity transformation

A hyperbolic singularity in the wave-function of $s$-wave interacting atoms is the root problem for any accurate numerical simulation. Here we apply the transcorrelated method, whereby the wave-function singularity is explicitly described by a two-body Jastrow factor, and then folded into the Hamiltonian via a similarity transformation. The resulting non-singular eigenfunctions are approximated by stochastic Fock-space diagonalisation with energy errors scaling with $1/M$ in the number $M$ of single-particle basis functions. The performance of the transcorrelated method is demonstrated on the example of strongly correlated fermions with unitary interactions. The current method provides the most accurate ground state energies so far for three and four fermions in a rectangular box with periodic boundary conditions.