Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundary layer techniques for deriving the effective properties of composite materials

In this paper we present mathematically rigorous derivations of asymptotic expansions of the eective electrical conductivity of periodic dilute composites in terms of the volume fraction occupied by the inclusions. Our derivations are based on layer potential techniques, and valid for high contrast mixtures and inclusions with Lipschitz boundaries. They are motivated by the practically important inverse problem of determining the volume fraction of a suspension of complicated shaped particles from boundary measurements of voltage potentials.
Summary statistics from large-scale gene-environment interaction studies for re-analysis and meta-analysis

Summary statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions (GEIs), there is a need for GEI-specific methods that manipulate and use summary statistics. We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study (GWIS) to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate more complex statistical models. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in GWIS in the UK Biobank. These programs help to maximize the value of summary statistics from diverse and complex GEI studies.
Linear Eulerian Extensions of Inhomogenous Random Graphs

The Eulerian extension number of any graph~\(H\) (i.e. the minimum number of edges needed to be added to make~\(H\) Eulerian) is at least~\(t(H),\) half the number of odd degree vertices of~\(H.\) In this paper we consider an inhomogenous random graph~\(G\) whose edge probabilities need not all be the same and use an iterative probabilistic method to obtain sufficient conditions for the Eulerian extension number of~\(G\) to grow \emph{linearly} with~\(t(G). \) We derive our conditions in terms of the average edge probabilities and edge density and also briefly illustrate our result with an example.
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development

In this work, we conduct a detailed analysis on the performance of legal-oriented pre-trained language models (PLMs). We examine the interplay between their original objective, acquired knowledge, and legal language understanding capacities which we define as the upstream, probing, and downstream performance, respectively. We consider not only the models’ size but also the pre-training corpora used as important dimensions in our study. To this end, we release a multinational English legal corpus (LeXFiles) and a legal knowledge probing benchmark (LegalLAMA) to facilitate training and detailed analysis of legal-oriented PLMs. We release two new legal PLMs trained on LeXFiles and evaluate them alongside others on LegalLAMA and LexGLUE. We find that probing performance strongly correlates with upstream performance in related legal topics. On the other hand, downstream performance is mainly driven by the model’s size and prior legal knowledge which can be estimated by upstream and probing performance. Based on these findings, we can conclude that both dimensions are important for those seeking the development of domain-specific PLMs.
Efficient Additive Gaussian Process Regression for Large-scale Data and Application to Analysis of Hourly-recorded NO$_2$ Concentrations in London (preprint)

This paper focuses on statistical modelling using additive Gaussian process (GP) models and their efficient implementation for large-scale spatio-temporal data with a multi-dimensional grid structure. To achieve this, we exploit the Kronecker product structures of the covariance kernel. While this method has gained popularity in the GP literature, the existing approach is limited to covariance kernels with a tensor product structure and does not allow flexible modelling and selection of interaction effects. This is considered an important component in spatio-temporal analysis. We extend the method to a more general class of additive GP models that accounts for main effects and selected interaction effects. Our approach allows for easy identification and interpretation of interaction effects. The proposed model is applied to the analysis of NO$_2$ concentrations during the COVID-19 lockdown in London. Our scalable method enables analysis of large-scale, hourly-recorded data collected from 59 different stations across the city, providing additional insights to findings from previous research using daily or weekly averaged data.
A morphological analysis of the substructures in radio relics

Recent observations of radio relics - diffuse radio emission in galaxy clusters - have revealed that these sources are not smooth but consist of structures in the form of threads and filaments. We investigate the origin of these filamentary structures and the role of projection effects. To this end, we have developed a tool that extracts the filamentary structures from background emission. Moreover, it is capable of studying both two-dimensional and three-dimensional objects. We apply our structure extractor to, both, observations and cosmological simulations of radio relics. Using Minkowski functionals, we determine the shape of the identified structures. In our 2D analysis, we find that the brightest structures in the observed and simulated maps are filaments. Our analysis of the 3D simulation data shows that radio relics do not consist of sheets but only of filaments and ribbons. Furthermore, we did not find any measurable projection effects that could hide any sheet-like structures in projection. We find that, both, the magnetic field and the shock front consist of filaments and ribbons that cause filamentary radio emission.
∂B nets: learning discrete functions by gradient descent

$\partial\mathbb{B}$ nets are differentiable neural networks that learn discrete boolean-valued functions by gradient descent. $\partial\mathbb{B}$ nets have two semantically equivalent aspects: a differentiable soft-net, with real weights, and a non-differentiable hard-net, with boolean weights. We train the soft-net by backpropagation and then `harden' the learned weights to yield boolean weights that bind with the hard-net. The result is a learned discrete function. `Hardening' involves no loss of accuracy, unlike existing approaches to neural network binarization. Preliminary experiments demonstrate that $\partial\mathbb{B}$ nets achieve comparable performance on standard machine learning problems yet are compact (due to 1-bit weights) and interpretable (due to the logical nature of the learnt functions).
Demonstrating Periscope: A Robotic Camera System to Support Remote Physical Collaboration

This paper proposes a demonstration of Periscope, a robotic camera system that allows two people to collaborate remotely on physical tasks. With Periscope, a worker co-located with a robotic arm can receive guidance from a remote helper who observes the workspace through a camera mounted on the robot. Periscope facilitates remote collaboration by providing the worker and the helper with shared visual information that enhances their verbal communication and coordination processes.
Reconfiguration of Time-Respecting Arborescences

An arborescence, which is a directed analogue of a spanning tree in an undirected graph, is one of the most fundamental combinatorial objects in a digraph. In this paper, we study arborescences in digraphs from the viewpoint of combinatorial reconfiguration, which is the field where we study reachability between two configurations of some combinatorial objects via some specified operations. Especially, we consider reconfiguration problems for time-respecting arborescences, which were introduced by Kempe, Kleinberg, and Kumar. We first prove that if the roots of the initial and target time-respecting arborescences are the same, then the target arborescence is always reachable from the initial one and we can find a shortest reconfiguration sequence in polynomial time. Furthermore, we show if the roots are not the same, then the target arborescence may not be reachable from the initial one. On the other hand, we show that we can determine whether the target arborescence is reachable form the initial one in polynomial time. Finally, we prove that it is NP-hard to find a shortest reconfiguration sequence in the case where the roots are not the same. Our results show an interesting contrast to the previous results for (ordinary) arborescences reconfiguration problems.
Content-based jewellery item retrieval using the local region-based histograms

Jewellery item retrieval is regularly used to find what people want on online marketplaces using a sample query reference image. Considering recent developments, due to the simultaneous nature of various jewelry items, various jewelry goods' occlusion in images or visual streams, as well as shape deformation, content-based jewellery item retrieval (CBJIR) still has limitations whenever it pertains to visual searching in the actual world. This article proposed a content-based jewellery item retrieval method using the local region-based histograms in HSV color space. Using five local regions, our novel jewellery classification module extracts the specific feature vectors from the query image. The jewellery classification module is also applied to the jewellery database to extract feature vectors. Finally, the similarity score is matched between the database and query features vectors to retrieve the jewellery items from the database. The proposed method performance is tested on publicly available jewellery item retrieval datasets, i.e. ringFIR and Fashion Product Images dataset. The experimental results demonstrate the dominance of the proposed method over the baseline methods for retrieving desired jewellery products.
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation

Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least eﬀort principle. Among many successful applications of OT in machine learning (ML), domain adaptation (DA) – a ﬁeld of study where the goal is to transfer a classiﬁer from one labelled domain to another similar, yet diﬀerent unlabelled or scarcely labelled domain – has been historically among the most investigated ones. This success is due to the ability of OT to provide both a meaningful discrepancy measure to assess the similarity of two domains’ distributions and a mapping that can project source domain data onto the target one. In this paper, we propose a principally new OT-based approach applied to DA that uses the closed-form solution of the OT problem given by an aﬃne mapping and learns an embedding space for which this solution is optimal and computationally less complex. We show that our approach works in both homogeneous and heterogeneous DA settings and outperforms or is on par with other famous baselines based on both traditional OT and OT in incomparable spaces. Furthermore, we show that our proposed method vastly reduces computational complexity.
Nonparametric data segmentation in multivariate time series via joint characteristic functions

Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.
Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide

Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence national audiences as some local stories may have wider implications for politics, environment or crime. Hence, detecting the exact geolocation and impact scope of local news is crucial for news recommendation systems. There are two fundamental things required in this process, (1) classify whether an article belongs to local news, and (2) identify the geolocation of the article and its scope of influence to recommend it to appropriate users. In this paper, we focus on the second step and propose (1) an efficient approach to determine the location and radius of local news articles, (2) a method to reconcile the user's location with the article's location, and (3) a metric to evaluate the quality of the local news feed. We demonstrate that our technique is scalable and effective in serving hyperlocal news to users worldwide.
Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table

Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset and code are publicly available.
Statistics of Magrathea exoplanets beyond the main sequence. Simulating the long-term evolution of circumbinary giant planets with TRES

Notwithstanding the tremendous growth of the exoplanetary field in the last decade, limited attention has been paid to the planets around binary stars. Circumbinary planets (CBPs) have been discovered primarily around Main Sequence (MS) stars. No exoplanet has been found orbiting double white dwarf (DWD) binaries yet. We modelled the long-term evolution of CBPs, throughout the life stages of their hosts, from MS to white dwarf (WD). Our goal is to provide the community with both theoretical constraints on CBPs evolution beyond the MS and the occurrence rates of planet survival. We further developed the publicly available Triple Evolution Simulation (TRES) code, implementing a variety of physical processes affecting substellar bodies. We then used this code to simulate the evolution, up to one Hubble time, of two synthetic populations of circumbinary giant planets. Each population has been generated using different priors for the planetary orbital parameters. In our simulated populations we identified several evolutionary categories, such as survived, merged, and destabilised systems. Our primary focus is those systems where the planet survived the entire system evolution and orbits a DWD binary, which we call"Magrathea"planets. We found that a significant fraction of simulated CBPs survive and become Magratheas. In the absence of multi-planet migration mechanisms, this category of planets is characterised by long orbital periods. Magrathea planets are a natural outcome of triple systems evolution, and they could be relatively common in the Galaxy. They can survive the death of their binary hosts if they orbit far enough to avoid engulfment and instabilities. Our results can ultimately be a reference to orient future observations of this uncharted class of planets and to compare different theoretical models.
Impact of errors in the magnetic field measurement on the precision determination of neutrino oscillation parameters at the proposed ICAL detector at INO

The magnetised iron calorimeter (ICAL) detector proposed at the India-based Neutrino Observatory will be a 51 kton detector made of 151 layers of 56 mm thick soft iron with 40 mm air gap in between where the RPCs, the active detectors, will be placed. The main goal of ICAL is to make precision measurements of the neutrino oscillation parameters, especially of the neutrino mass ordering, using the atmospheric neutrinos as source. The charged current interactions of the atmospheric muon neutrinos and anti-neutrinos in the detector produce charged muons. The magnetic field, with a maximum value of ∼ 1.5 T in the central region of ICAL, is a critical component since it will be used to distinguish the charges and determine the momentum and direction of these muons. It is difficult to measure the magnetic field inside the iron. The existing methods can only estimate the internal field and hence will be prone to error. This paper presents the first simulations study of the effect of errors in the measurement of the magnetic field in ICAL on its physics potential, especially the neutrino mass ordering and precision measurement of oscillation parameters in the 2–3 sector. The study is a GEANT4-based analysis, using measurements of the magnetic field at the prototype ICAL detector. We find that there is only a small effect on the determination of the mass ordering. While local fluctuations in the magnetic field measurement are well-tolerated, calibration errors must remain well within 5% to retain good precision determination of the parameters sin2 θ 23 and Δm 2 32.
Professional Ethics by Design: Co-creating Codes of Conduct for Computational Practice

This paper deals with the importance of developing codes of conduct for practitioners--be it journalists, doctors, attorneys, or other professions--that are encountering ethical issues when using computation, but do not have access to any framework of reference as to how to address those. At the same time, legal and technological developments are calling for establishing such guidelines, as shown in the European Union's and the United States' efforts in regulating a wide array of artificial intelligence systems, and in the resurgence of rule-based models through 'neurosymbolic' AI, a hybrid format that combines them with neural methods. Against this backdrop, we argue for taking a design-inspired approach when encoding professional ethics into a computational form, so as to co-create codes of conduct for computational practice across a wide range of fields.
Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing

We detect the cross-correlation between 2.7 million DESI quasar targets across 14,700 deg2 (180 quasars deg-2) and Planck 2018 CMB lensing at ∼30σ. We use the cross-correlation on very large scales to constrain local primordial non-Gaussianity via the scale dependence of quasar bias. The DESI quasar targets lie at an effective redshift of 1.51 and are separated into four imaging regions of varying depth and image quality. We select quasar targets from Legacy Survey DR9 imaging, apply additional flux and photometric redshift cuts to improve the purity and reduce the fraction of unclassified redshifts, and use early DESI spectroscopy of 194,000 quasar targets to determine their redshift distribution and stellar contamination fraction (2.6%). Due to significant excess large-scale power in the quasar autocorrelation, we apply weights to mitigate contamination from imaging systematics such as depth, extinction, and stellar density. We use realistic contaminated mocks to determine the greatest number of systematic modes that we can fit, before we are biased by overfitting and spuriously remove real power. We find that linear regression with one to seven imaging templates removed per region accurately recovers the input cross-power, f NL and linear bias. As in previous analyses, our f NL constraint depends on the linear primordial non-Gaussianity bias parameter, bϕ = 2(b - p)δc assuming universality of the halo mass function. We measure f NL = -26+45 -40 with p = 1.6 (f NL = -18+29 -27 with p = 1.0), and find that this result is robust under several systematics tests. Future spectroscopic quasar cross-correlations with Planck lensing can tighten the f NL constraints by a factor of 2 if they can remove the excess power on large scales in the quasar auto power spectrum.
Smoothed empirical likelihood estimation and automatic variable selection for an expectile high-dimensional model with possibly missing response variable

We consider a linear model which can have a large number of explanatory variables, the errors with an asymmetric distribution or some values of the explained variable are missing at random. In order to take in account these several situations, we consider the non parametric empirical likelihood (EL) estimation method. Because a constraint in EL contains an indicator function then a smoothed function instead of the indicator will be considered. Two smoothed expectile maximum EL methods are proposed, one of which will automatically select the explanatory variables. For each of the methods we obtain the convergence rate of the estimators and their asymptotic normality. The smoothed expectile empirical log-likelihood ratio process follow asymptotically a chi-square distribution and moreover the adaptive LASSO smoothed expectile maximum EL estimator satisfies the sparsity property which guarantees the automatic selection of zero model coefficients. In order to implement these methods, we propose four algorithms.
Beware of diffusion models for synthesizing medical images - A comparison with GANs in terms of memorizing brain tumor images

Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as FID and IS are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train Style-GAN and diffusion models, using BRATS20 and BRATS21 datasets, to synthesize brain tumor images, and measure the correlation between the synthetic images and all training images. Our results show that diffusion models are much more likely to memorize the training images, especially for small datasets. Researchers should be careful when using diffusion models for medical imaging, if the final goal is to share the synthetic images.