Miracle-dz/UniFAR-FTUs huggingface dataset:
Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout

State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents. But these solutions are still struggling when it comes to longer, real-world documents with the information encoded in the spatial structure of the document, such as page elements like tables, forms, headers, openings or footers; complex page layout or presence of multiple pages. To encourage progress on deeper and more complex Information Extraction (IE) we introduce a new task (named Kleister) with two new datasets. Utilizing both textual and structural layout features, an NLP system must find the most important information, about various types of entities, in long formal documents. We propose Pipeline method as a text-only baseline with different Named Entity Recognition architectures (Flair, BERT, RoBERTa). Moreover, we checked the most popular PDF processing tools for text extraction (pdf2djvu, Tesseract and Textract) in order to analyze behavior of IE system in presence of errors introduced by these tools.
Individual Claims Forecasting with Bayesian Mixture Density Networks

We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging. The proposed approach enables incorporating claims information from both structured and unstructured data sources, producing multi-period cash flow forecasts, and generating different scenarios of future payment patterns. We implement and evaluate the modeling framework using publicly available data.
Drone Based RGBT Vehicle Detection and Counting: A Challenge

Camera-equipped drones can capture targets on the ground from a wider field of view than static cameras or moving sensors over the ground. In this paper we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform. The images in the benchmark were captured over various urban areas, which include different types of urban roads, residential areas, parking lots, highways, etc., from day to night. Specifically, DroneVehicle consists of 15,532 pairs of images, i.e., RGB images and infrared images with rich annotations, including oriented object bounding boxes, object categories, etc. With intensive amount of effort, our benchmark has 441,642 annotated instances in 31,064 images. As a large-scale dataset with both RGB and thermal infrared (RGBT) images, the benchmark enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. In particular, we design two popular tasks with the benchmark, including object detection and object counting. All these tasks are extremely challenging in the proposed dataset due to factors such as illumination, occlusion, and scale variations. We hope the benchmark largely boost the research and development in visual analysis on drone platforms. The DroneVehicle dataset can be download from this https URL.
Knowledge Graphs

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.
A Generalization of the Classical Kelly Betting Formula to the Case of Temporal Correlation

For sequential betting games, Kelly’s theory, aimed at maximization of the logarithmic growth of one’s account value, involves optimization of the so-called betting fraction <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>. In this letter, we extend the classical formulation to allow for temporal correlation among bets. To demonstrate the potential of this new paradigm, for simplicity of exposition, we mainly address the case of a coin-flipping game with even-money payoff. To this end, we solve a problem with <italic>memory depth</italic> <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>. By this, we mean that the outcomes of coin flips are no longer assumed to be i.i. d. random variables. Instead, the probability of heads on flip <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> depends on previous flips <inline-formula> <tex-math notation="LaTeX">$k-1,k-2,\ldots, k-m$ </tex-math></inline-formula>. For the simplest case of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> flips, with <inline-formula> <tex-math notation="LaTeX">$m = 1$ </tex-math></inline-formula>, we obtain a closed form solution <inline-formula> <tex-math notation="LaTeX">$K_{n}$ </tex-math></inline-formula> for the optimal betting fraction. This generalizes the classical result for the memoryless case. That is, instead of fraction <inline-formula> <tex-math notation="LaTeX">$K^{*} = 2p-1$ </tex-math></inline-formula> which pervades the literature for a coin with probability of heads <inline-formula> <tex-math notation="LaTeX">$p\geq 1/2$ </tex-math></inline-formula>, our new fraction <inline-formula> <tex-math notation="LaTeX">$K_{n}$ </tex-math></inline-formula> depends on both <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and the parameters associated with the temporal correlation. Generalizations of these results for <inline-formula> <tex-math notation="LaTeX">$m > 1$ </tex-math></inline-formula> and numerical simulations are also included. Finally, we indicate how the theory extends to time-varying feedback and alternative payoff distributions.
Predictions from warped flavor dynamics based on the 
T′
 family group

We propose a realistic five-dimensional warped scenario where all standard model fields propagate in the bulk. The assumed $T'$ flavor symmetry is broken on the branes by flavon fields, providing a consistent scenario where fermion mass hierarchies are accounted for by adequate choices of the bulk mass parameters, while quark and lepton mixing angles are restricted by the flavor symmetry. Neutrino mixing parameters and the Dirac CP violation phase are all described in terms of just two independent parameters. This leads to predictions for the neutrino mixing angles and the Dirac CP phase, as well as a $\rm 0\nu\beta\beta$ decay rate within reach of upcoming experiments. The scheme provides a good global description of flavor observables also in the quark sector.
Spherical Principal Curves

This paper presents a new approach for dimension reduction of data observed on spherical surfaces. Several dimension reduction techniques have been developed in recent years for non-euclidean data analysis. As a pioneer work, (Hauberg 2016) attempted to implement principal curves on Riemannian manifolds. However, this approach uses approximations to process data on Riemannian manifolds, resulting in distorted results. This study proposes a new approach to project data onto a continuous curve to construct principal curves on spherical surfaces. Our approach lies in the same line of (Hastie and Stuetzle et al. 1989) that proposed principal curves for data on euclidean space. We further investigate the stationarity of the proposed principal curves that satisfy the self-consistency on spherical surfaces. The results on the real data analysis and simulation examples show promising empirical characteristics of the proposed approach.
Pietsch correspondence for symmetric functionals on Calkin operator spaces associated with semifinite von Neumann algebras

In this paper we extend the Pietsch correspondence for ideals of compact operators and traces on them to the semifinite setting. We prove that a shift-monotone space $E(\Z)$ of sequences indexed by $\Z$ defines a Calkin space $E(\cM,\tau)$ of $\tau$-measurable operators affiliated with a semifinite von Neumann algebra $\cM$ equipped with a faithful normal semifinite trace $\tau$. Furthermore, we show that shift-invariant functionals on $E(\Z)$ generate symmetric functionals on $E(\cM,\tau)$. In the special case, when the algebra $\cM$ is atomless or atomic with atoms of equal trace, the converse also holds and we have a bijective correspondence between all shift-monotone spaces $E(\Z)$ and Calkin spaces $E(\cM,\tau)$ as well as a bijective correspondence between shift-invariant functionals on $E(\Z)$ and symmetric functionals on $E(\cM,\tau)$. The bijective correspondence $E(\Z)\leftrightarrows E(\cM,\tau)$ extends to a correspondence between complete symmetrically $\Delta$-normed spaces $E(\cM,\tau)$ and complete $\Delta$-normed shift-monotone spaces $E(\Z)$.
Event-Based Angular Velocity Regression with Spiking Networks

Spiking Neural Networks (SNNs) are bio-inspired networks that process information conveyed as temporal spikes rather than numeric values. An example of a sensor providing such data is the event-camera. It only produces an event when a pixel reports a significant brightness change. Similarly, the spiking neuron of an SNN only produces a spike whenever a significant number of spikes occur within a short period of time. Due to their spike-based computational model, SNNs can process output from event-based, asynchronous sensors without any pre-processing at extremely lower power unlike standard artificial neural networks. This is possible due to specialized neuromorphic hardware that implements the highly-parallelizable concept of SNNs in silicon. Yet, SNNs have not enjoyed the same rise of popularity as artificial neural networks. This not only stems from the fact that their input format is rather unconventional but also due to the challenges in training spiking networks. Despite their temporal nature and recent algorithmic advances, they have been mostly evaluated on classification problems. We propose, for the first time, a temporal regression problem of numerical values given events from an event-camera. We specifically investigate the prediction of the 3- DOF angular velocity of a rotating event-camera with an SNN. The difficulty of this problem arises from the prediction of angular velocities continuously in time directly from irregular, asynchronous event-based input. Directly utilising the output of event-cameras without any pre-processing ensures that we inherit all the benefits that they provide over conventional cameras. That is high-temporal resolution, high-dynamic range and no motion blur. To assess the performance of SNNs on this task, we introduce a synthetic event-camera dataset generated from real-world panoramic images and show that we can successfully train an SNN to perform angular velocity regression.
Gapless and Massive 1D Singlet Dispersion Channel in Infinite Spin-1/2 Ladders ---Infinite Quasi-1D Entanglement Perturbation Theory for Excitation

We solve for the elementary excitation in infinite quasi-1D quantum lattices by extending the recently developed infinite quasi-1D entanglement perturbation theory. The wave function of an excited state is variationally determined by optimizing superposition of cluster operation, each of which is composed of simultaneous on-site operation inside a block of lattice sites, on the ground state in a form of plane wave. The excitation energy with respect to the wave number gives the spectra for an elementary excitation. Our method is artificial broadening free and is adaptive for various quasi-particle pictures. Using the triplet spectrum, the application to $\infty$-by-$N$ antiferromagnetic spin-$\frac{1}{2}$ ladders for $N=2, 4, 6, 8$, and $10$ confirms a previous report that there is a quantum dimensional transition, namely, the lattice transits from quasi-1D to 2D at a finite critical value $N_c=10$. The massless triplet dispersion at $\left( \pi, \pi \right)$ sees a vanishing gap. Our results detect the anomaly at $\left(\pi,0\right)$ in the triplet spectrum, agreeing well with the inelastic neutron scattering measurement of a macroscopic sample. Surprisingly, our results also reveal a gapless and massive 1D singlet dispersion channel that is much lower than the triplet excitation. We note, however, the dimensional transition is determined by the massless triplet dispersion.
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first and second order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real-world phenomena, we introduce an approach that merges higher order spectral analysis (HOSA) with deep convolutional neural networks (CNNs) for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.
Design and theoretical performance analysis of a fully distributed detection algorithm with dependent observations in WSN

In this work we consider the problem of distributed detection with spatially correlated measurements at each sensor. A cooperative distributed detection algorithm is considered for reducing the number of communications between sensors and thus make an efficient use of the energy budget of a wireless sensor network (WSN). The problem is formulated as a composite hypothesis test with unknown parameters leading naturally to the use of the Generalized Likelihood Ratio (GLR). As the sensors of the network observe statistically spatial dependent samples modeled by a general pdf, which leads to severe difficulties in implementing a fully distributed detection procedure, some simplifications are proposed which allow to run a simple consensus algorithm for making a decision about the state of nature. We also compute the asymptotic distribution of the proposed statistic to characterize its performance. Interestingly, despite the fact that our proposal is more simple and efficient to implement that the GLR test, we find non-trivial scenarios for which it outperforms the latter, even in finite length regimes.
Mathematical modelling of plasmonic strain sensors

Abstract We provide a mathematical analysis for a metasurface constructed of plasmonic nanoparticles mounted periodically on the surface of a microcapsule. We derive an effective transmission condition, which exhibits resonances depending on the inter-particle distance. When the microcapsule is deformed, the resonances are shifted. We fully characterize the dependence of these resonances on the deformation of the microcapsule, enabling the detection of strains at the microscale level. We present numerical simulations to validate our results.
Comparing Rewinding and Fine-tuning in Neural Network Pruning

Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining technique, fine-tuning, trains the unpruned weights from their final trained values using a small fixed learning rate. In this paper, we compare fine-tuning to alternative retraining techniques. Weight rewinding (as proposed by Frankle et al., (2019)), rewinds unpruned weights to their values from earlier in training and retrains them from there using the original training schedule. Learning rate rewinding (which we propose) trains the unpruned weights from their final values using the same learning rate schedule as weight rewinding. Both rewinding techniques outperform fine-tuning, forming the basis of a network-agnostic pruning algorithm that matches the accuracy and compression ratios of several more network-specific state-of-the-art techniques.
Application of Deep Neural Networks to assess corporate Credit Rating

Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selected subset of features, or if it is better to allow the algorithms to select features themselves. Second, is the temporal aspect inherent in financial data important for the results obtained by a machine learning algorithm? Third, is there a particular neural network architecture that consistently outperforms others with respect to input features, sectors and holdout set? We create several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedure.
Endpoint of the up-down instability in precessing binary black holes

Binary black holes in which both spins are aligned with the binary's orbital angular momentum do not precess. However, the up-down configuration, in which the spin of the heavier (lighter) black hole is aligned (anti-aligned) with the orbital angular momentum, is unstable to spin precession at small orbital separations. We first cast the spin precession problem in terms of a simple harmonic oscillator and provide a cleaner derivation of the instability onset. Surprisingly, we find that following the instability, up-down binaries do not disperse in the available parameter space but evolve toward precise endpoints. We then present an analytic scheme to locate these final configurations and confirm them with numerical integrations. Namely, unstable up-down binaries approach mergers with the two spins coaligned with each other and equally misaligned with the orbital angular momentum. Merging up-down binaries relevant to LIGO/Virgo and LISA may be detected in these endpoint configurations if the instability onset occurs prior to the sensitivity threshold of the detector. As a by-product, we obtain new generic results on binary black hole spin-orbit resonances at 2nd~post-Newtonian order. We finally apply these findings to a simple astrophysical population of binary black holes where a formation mechanism aligns the spins without preference for co- or counteralignment, as might be the case for stellar-mass black holes embedded in the accretion disk of a supermassive black hole.
Fake Generated Painting Detection Via Frequency Analysis

With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.
Scaling solutions and weak gravity in dark energy with energy and momentum couplings

We argue that the ΛCDM tensions of the Hubble-Lemaȋtre expansion rate H0 and the clustering normalization σ8 can be eased, at least in principle, by considering an interaction between dark energy and dark matter in such a way to induce a small and positive early effective equation of state and a weaker gravity. For a dark energy scalar field ϕ interacting with dark matter through an exchange of both energy and momentum, we derive a general form of the Lagrangian allowing for the presence of scaling solutions. In a subclass of such interacting theories, we show the existence of a scaling ϕ-matter-dominated-era (ϕMDE) which can potentially alleviate the H0 tension by generating an effective high-redshift equation of state. We also study the evolution of perturbations for a model with ϕMDE followed by cosmic acceleration and find that the effective gravitational coupling relevant to the linear growth of large-scale structures can be smaller than the Newton gravitational constant G at low redshifts. The momentum exchange between dark energy and dark matter plays a crucial role for realizing weak gravity, while the energy transfer is also required for the existence of ϕMDE.
Measuring the Mere Measurement Effect in Non-Experimental Field Settings

The mere measurement effect is a well-documented effect in laboratory studies and has recently been documented in several field surveys. In this paper, we identify two major obstacles to measuring the mere measurement effect in a non-experimental, field setting. These are: Who is asked to participate in a survey? Who agrees to respond to a survey? Firms often target surveys at particular consumers and this induces an effect we refer to as targeting bias. In addition, consumers who respond to a survey may not be representative of the overall population and this may create response bias. In this paper, we present a methodology that simultaneously controls for both targeting and response bias in a non-experimental, field setting. In developing our methodology, we identify four different ways to measure the mere measurement effect. We show how these metrics relate to the amount of information a researcher has about the surveyed customers. We demonstrate our methodology on a dataset from a direct marketing firm that regularly surveys its customers. The results show that failure to control for targeting and response biases leads to overestimates of the impact of survey response on purchase frequency. Without controlling for these biases, our model predicts a positive mere measurement effect of 15%. After controlling for these biases, we find a negative mere measurement effect; purchase rates decrease by over 17% following completion of a survey. The model also yields estimates of the mere measurement effect for different customer