Miracle-dz/UniFAR-FTUs huggingface dataset:
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity

Tables are a popular and efficient means of presenting structured information. They are used extensively in various kinds of documents including web pages. Tables display information as a two-dimensional matrix, the semantics of which is conveyed by a mixture of structure (rows, columns), headers, caption, and content. Recent research has started to consider tables as first class objects, not just as an addendum to texts, yielding interesting results for problems like table matching, table completion, or value imputation. All of these problems inherently rely on an accurate measure for the semantic similarity of two tables. We present TabSim, a novel method to compute table similarity scores using deep neural networks. Conceptually, TabSim represents a table as a learned concatenation of embeddings of its caption, its content, and its structure. Given two tables in this representation, a Siamese neural network is trained to compute a score correlating with the tables’ semantic similarity. To train and evaluate our method, we created a gold standard corpus consisting of 1500 table pairs extracted from biomedical articles and manually scored regarding their degree of similarity, and adopted two other corpora originally developed for a different yet similar task. Our evaluation shows that TabSim outperforms other table similarity measures on average by app. 7% pp F1-score in a binary similarity classification setting and by app. 1.5% pp in a ranking scenario. The codes and resources introduced here are available at https://github.com/Marhabibi/TabSim.
Physical Layer Security in Cooperative NOMA Hybrid VLC/RF Systems

Integrating visible light communication (VLC) and radio-frequency (RF) networks can improve the performance of communication systems in terms of coverage and data rates. However, adding RF links to VLC networks weakens the secrecy performance due to the broadcast and ubiquitous nature of RF links. This paper studies the physical layer security (PLS) in cooperative non-orthogonal multiple access (CoNOMA) hybrid VLC/RF systems. Consider a VLC system, where two entrusted users close to a VLC access point (AP) help an out-of-coverage legitimate user using RF signals in the presence of an eavesdropper. The AP transmits data to both entrusted users and the legitimate user using the principle of NOMA, where the entrusted users harvest energy from the received light intensity, decode the legitimate user's message, forward it using a RF link, and then decode their messages. It is required to maximize the secrecy rate at the legitimate user under quality-of-service (QoS) constraints using beamforming and DC-bias and power allocation. Different solutions are proposed for both active and passive eavesdropper cases, using semidefinite relaxation, zero-forcing, beamforming, and jamming. Numerical results compare between the different proposed approaches and show how the proposed approaches contribute in improving the secrecy performance of the proposed model.
Towards Better Bus Networks: A Visual Analytics Approach

Bus routes are typically updated every 3–5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics
Optimization with learning-informed differential equation constraints and its applications

Inspired by applications in optimal control of semilinear elliptic partial differential equations and physics-integrated imaging, differential equation constrained optimization problems with constituents that are only accessible through data-driven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machine-learned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided.
X-rays observations of a super-Chandrasekhar object reveal an ONe and a CO white dwarf merger product embedded in a putative SN Iax remnant

The merger of two white dwarfs (WDs) is a natural outcome of the evolution of many binary stars. Recently, a WD merger product, IRAS 00500+6713, was identified. IRAS 00500+6713 consists of a central star embedded in a circular nebula. The analysis of the optical spectrum of the central star revealed that it is hot, hydrogen, and helium free, and it drives an extremely fast wind with a record breaking speed. The nebula is visible in infrared and in the [O III] λ5007 Å line images. No nebula spectroscopy was obtained prior to our observations. Here we report the first deep X-ray imaging spectroscopic observations of IRAS 00500+6713. Both the central star and the nebula are detected in X-rays, heralding the WD merger products as a new distinct type of strong X-ray sources. Low-resolution X-ray spectra reveal large neon, magnesium, silicon, and sulfur enrichment of the central star and the nebula. We conclude that IRAS 00500+6713 resulted from a merger of an ONe and a CO WD, which supports earlier suggestion for a super-Chandrasekhar mass of this object. X-ray analysis indicates that the merger was associated with an episode of carbon burning and possibly accompanied by an SN Iax. In X-rays, we observe the point source associated with the merger product while the surrounding diffuse nebula is a supernova remnant. IRAS 00500+6713 will likely terminate its evolution with another peculiar Type I supernova, where the final core collapse to a neutron star might be induced by electron captures.
Cosmography using strong-lensing systems and cosmic chronometers

Using a new sub-sample of observed strong gravitational lens systems, for the first time, we present the equation for the angular diameter distance in the y-redshift scenario for cosmography and use it to test the cosmographic parameters. In addition, we also use the observational Hubble data from cosmic chronometers and a joint analysis of both data are performed. Among the most important conclusions are that this new analysis for cosmography using strong-lensing systems (SLSs) is equally competitive to constrain the cosmographic parameters as others presented in literature. Additionally, we present the reconstruction of the effective equation of state inferred from our samples, showing that at z = 0 those reconstructions from SLSs and joint analysis are in concordance with the standard model of cosmology.
Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance

Abstract When evaluating and comparing models using leave-one-out cross-validation (LOO-CV), the uncertainty of the estimate is typically assessed using the variance of the sampling distribution. Considering the uncertainty is important, as the variability of the estimate can be high in some cases. Previous studies show that no general unbiased variance estimator can be constructed, that would apply for any utility or loss measure and any model. We show that it is possible to construct an unbiased estimator considering a specific predictive performance measure and model. We demonstrate an unbiased sampling distribution variance estimator for the Bayesian normal model with fixed model variance using the expected log pointwise predictive density (elpd) utility score. This example demonstrates that it is possible to obtain improved, problem-specific, unbiased estimators for assessing the uncertainty in LOO-CV estimation.
An Embedded System for Monitoring Industrial Air Dehumidifiers using a Mobile Android Application for IEEE 802.11 Networks

The constant technological evolution allowed significant advances and improvements in the processes of industries, mainly in areas that demand greater control and environmental air efficiency. In this way, Embedded Systems allows the development of products and services that aim to solve or propose solutions in these industrial environments. This article presents the development of an Embedded System with a Programmable Logic Controller (PLC) and Arduino for industrial air dehumidifier, which allows the monitoring of failures remotely from a reliable data communication in a mobile application for Android operating system (OS) on a wireless network IEEE 802.11. As a result, a prototype of the test bench for the Embedded System is presented in which the main parameters of temperature sensors and operating conditions of the dehumidifiers are checked.
Revisiting the spectral and timing properties of 4U 1909+07 with NuSTAR and Astrosat

We present the results obtained from the analysis of high-mass X-ray binary pulsar 4U 1909+07 using NuSTAR and Astrosat observations in July 2015 and 2017, respectively. X-ray pulsations at ≈604 s are clearly detected in our study. Based on the long-term spin-frequency evolution, the source is found to spun-up in the last 17 yr. We observed a strongly energy-dependent pulse profile that evolved from a complex broad structure in soft X-rays into a profile with a narrow emission peak followed by a plateau in energy ranges above 20 keV. This behaviour ensured a positive correlation between the energy and pulse fraction. The pulse profile morphology and its energy evolution are almost similar during both the observations, suggesting a persistent emission geometry of the pulsar over time. The broad-band energy spectrum of the pulsar is approximated by an absorbed high-energy exponential cut-off power-law model with iron emission lines. In contrast to the previous report, we found no statistical evidence for the presence of cyclotron absorption features in the X-ray spectra. We performed phase-resolved spectroscopy using data from the NuSTAR observation. Our results showed a clear signature of absorbing material at certain pulse phases of the pulsar. These findings are discussed in terms of stellar wind distribution and its effect on the beam geometry of this wind-fed accreting neutron star. We also reviewed the subsonic quasi-spherical accretion theory and its implication on the magnetic field of 4U 1909+07 depending on the global spin-up rate.
LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system’s expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems.
GRB spectrum from gradual dissipation in a magnetized outflow

Modelling of many gamma-ray burst prompt emission spectra sometimes requires a (quasi) thermal spectral component in addition to the Band function that sometimes leads to a double-hump spectrum, the origin of which remains unclear. In photospheric emission models, a prominent thermal component broadened by sub-photospheric dissipation is expected to be released at the photospheric radius, r_(ph) ∼10¹² cm. We consider an ultra-relativistic strongly magnetized steady outflow with a striped-wind magnetic-field structure undergoing gradual and continuous magnetic energy dissipation at r < r_s that heats and accelerates the flow to a bulk Lorentz factor Γ(r) = Γ_∞min [1, (r/r_s)^(1/3)], where typically r_(ph) < r_s. Similar dynamics and energy dissipation rates are also expected in highly variable magnetized outflows without stripes/field-reversals. Two modes of particle energy injection are considered: (a) power-law electrons, e.g. accelerated by magnetic reconnection, and (b) distributed heating of all electrons (and e±-pairs), e.g. due to magnetohydrodynamic instabilities. Steady-state spectra are obtained using a numerical code that evolves coupled kinetic equations for a photon-electron-positron plasma. We find that (i) the thermal component consistently peaks at (1+z)E_(pk) ∼ 0.2−1 MeV, for a source at redshift z, and becomes sub-dominant if the total injected energy density exceeds the thermal one, (ii) power-law electrons cool mainly by synchrotron emission whereas mildly relativistic and almost monoenergetic electrons in the distributed heating scenario cool by Comptonization on thermal peak photons, (iii) both scenarios can yield a low-energy break, and (iv) the ∼0.5(1+z)⁻¹ keV X-ray emission is suppressed in scenario (a), whereas it is expected in scenario (b). Energy-dependent linear polarization can differentiate between the two particle heating scenarios.
Adaptive Context-Aware Multi-Modal Network for Depth Completion

Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image. The observed pixels provide the significant guidance for the recovery of the unobserved pixels’ depth. However, due to the sparsity of the depth data, the standard convolution operation, exploited by most of existing methods, is not effective to model the observed contexts with depth values. To address this issue, we propose to adopt the graph propagation to capture the observed spatial contexts. Specifically, we first construct multiple graphs at different scales from observed pixels. Since the graph structure varies from sample to sample, we then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Furthermore, considering the mutli-modality of input data, we exploit the graph propagation on the two modalities respectively to extract multi-modal representations. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. The proposed strategy preserves the original information for one modality and also absorbs complementary information from the other through learning the adaptive gating weights. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks, i.e., KITTI and NYU-v2, and at the same time has fewer parameters than latest models. Our code is available at: https://github.com/sshan-zhao/ACMNet.
Boundary Uncertainty in a Single-Stage Temporal Action Localization Network

In this paper, we address the problem of temporal action localization with a single stage neural network. In the proposed architecture we model the boundary predictions as uni-variate Gaussian distributions in order to model their uncertainties, which is the first in this area to the best of our knowledge. We use two uncertainty-aware boundary regression losses: first, the Kullback-Leibler divergence between the ground truth location of the boundary and the Gaussian modeling the prediction of the boundary and second, the expectation of the $\ell_1$ loss under the same Gaussian. We show that with both uncertainty modeling approaches improve the detection performance by more than $1.5\%$ in mAP@tIoU=0.5 and that the proposed simple one-stage network performs closely to more complex one and two stage networks.
Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter

The formation of majorities in public discussions often depends on individuals who shift their opinion over time. The detection and characterization of these type of individuals is therefore extremely important for political analysis of social networks. In this paper, we study changes in individual's affiliations on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks. We identified communities with explicit political orientation and topics of discussion associated to them which provide the topological representation of the political map on Twitter in the analyzed periods. With that data, we present a machine learning framework for social media users classification which efficiently detects "shifting users" (i.e. users that may change their affiliation over time). Moreover, this machine learning framework allows us to identify not only which topics are more persuasive (using low dimensional topic embedding), but also which individuals are more likely to change their affiliation given their topological properties in a Twitter graph.
A Kernel Two-Sample Test for Functional Data

We propose a nonparametric two-sample test procedure based on Maximum Mean Discrepancy (MMD) for testing the hypothesis that two samples of functions have the same underlying distribution, using a kernel defined on a function space. This construction is motivated by a scaling analysis of the efficiency of MMD-based tests for datasets of increasing dimension. Theoretical properties of kernels on function spaces and their associated MMD are established and employed to ascertain the efficacy of the newly proposed test, as well as to assess the effects of using functional reconstructions based on discretised function samples. The theoretical results are demonstrated over a range of synthetic and real world datasets.
Influence of Lobesia botrana larvae on field severity of Botrytis rot of grape berries

A 3-yr study was conducted in the vineyards of Bordeaux to assess the effects of the last two generations of Lobesia botrana larvae on the development of Botrytis rot. In the absence of fungicides to control B. cinerea, second-generation larvae favored the initiation of Botrytis rot on grapes in midseason (i.e., at the beginning of veraison). At this point, the number of second-generation larvae per cluster was highly correlated with the number of early disease centers. At harvest, the effect of second-generation larvae alone was also measurable and resulted in a significant increase in disease severity (.)
Analytic Eigensystems for Isotropic Membrane Energies

We extend the approach of [Smith et al. 2019] to derive analytical expressions for the eigenvalues and eigenmatrices of an isotropic membrane energy density function $\psi : \mathbb{R}^{3x2} \to \mathbb{R}$. Clamping the eigenvalue expressions to be positive for each quadrature point of a finite element membrane simulation guarantees a positive semi-definite Hessian for the full discrete membrane energy, enabling an efficient Newton-type simulation.
An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered.