Miracle-dz/UniFAR-FTUs huggingface dataset:
GriTS: Grid table similarity metric for table structure recognition

In this paper, we propose a new class of metric for table structure recognition (TSR) evaluation, called grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form as a matrix. To create a similarity measure between matrices, we generalize the two-dimensional largest common substructure (2D-LCS) problem, which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and propose a polynomial-time heuristic for solving it. This algorithm produces both an upper and a lower bound on the true similarity between matrices. We show using evaluation on a large real-world dataset that in practice there is almost no difference between these bounds. We compare GriTS to other metrics and empirically validate that matrix similarity exhibits more desirable behavior than alternatives for TSR performance evaluation. Finally, GriTS unifies all three subtasks of cell topology recognition, cell location recognition, and cell content recognition within the same framework, which simplifies the evaluation and enables more meaningful comparisons across different types of TSR approaches. Code will be released at https://github.com/microsoft/table-transformer.
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining.
SCALABLE SEMANTIC VIRTUAL MACHINE FRAMEWORK FOR LANGUAGE-AGNOSTIC STATIC ANALYSIS

The more static program analysis spreads in the industry, the clearer it becomes that its real-world usage requires something more than just optimized algorithms to make execution fast. Distribution of computations to cluster nodes is one of the viable ways for speeding up the process. The research demonstrates one of the approaches for organizing distributed static analysis – “Semantic Virtual Machines”, which is based upon symbolic execution of language-agnostic intermediate codes of programs. These codes belong to the developed language for program representation with semantic objects behind. As objects are fully serializable, they can be saved to and later retrieved from a distributed database. That opens up a possibility for making an analyzer spread to many cluster nodes. Semantic Hypervisor is the core of the system which acts as a master node for managing runnable virtual machines and also a relay between them and a semantics storage. Main efforts are put into the development of intercommunication mechanisms which can be effective in most of the scenarios, as well as into integration of the approach in existing static analyzer. The study shows the architecture, describes its advantages and disadvantages, suggests solutions for the biggest problems observed during research. The empirical study shows improved performance compared to a single node run, and the result is almost linearly scalable due to the high locality of data. The main result is the created scalable and unified language-agnostic architecture for static analysis with semantics data in a distributed storage behind of it. The significance of the research is in high achieved performance level alongside with a clean architecture.
Characterizing High-Quality Test Methods: A First Empirical Study

To assess the quality of a test suite, one can rely on mutation testing, which computes whether the overall test cases are adequately exercising the covered lines. However, this high level of granularity may overshadow the quality of individual test methods. In this paper, we propose an empirical study to assess the quality of test methods by relying on mutation testing at the method level. We find no major differences between high-quality and low-quality test methods in terms of size, number of asserts, and modifications. In contrast, high-quality test methods are less affected by critical test smells. Finally, we discuss practical implications for researchers and practitioners.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and nonlinear forward models for computational MRI and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play (PnP) methods, generative models, and unrolled networks. We highlight domain-specific challenges, such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and nonlinear forward models. Finally, we discuss common issues and open challenges, and we draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.
Being KLEVER at cosmic noon: ionised gas outflows are inconspicuous in low-mass star-forming galaxies but prominent in massive AGN hosts

We investigate the presence of ionised gas outflows in a sample of 141 main-sequence star-forming galaxies at 1.2 < z < 2.6 from the KLEVER (KMOS Lensed Emission Lines and VElocity Review) survey. Our sample covers an exceptionally wide range of stellar masses, 8.1 < log (M⋆/M⊙) < 11.3, pushing outflow studies into the dwarf regime thanks to gravitationally lensed objects. We stack optical rest-frame emission lines (Hβ, [OIII], Hα and [NII]) in different mass bins and seek for tracers of gas outflows by using a novel, physically motivated method that improves over the widely used, simplistic double Gaussian fitting. We compare the observed emission lines with the expectations from a rotating disc (disc+bulge for the most massive galaxies) model, whereby significant deviations are interpreted as a signature of outflows. We find clear evidence for outflows in the most massive, log (M⋆/M⊙) > 10.8, AGN-dominated galaxies, suggesting that AGNs may be the primary drivers of these gas flows. Surprisingly, at log (M⋆/M⊙) ≤ 9.6, the observed line profiles are fully consistent with a rotating disc model, indicating that ionised gas outflows in dwarf galaxies might play a negligible role even during the peak of cosmic star-formation activity. Finally, we find that the observed mass loading factor scales with stellar mass as expected from the TNG50 cosmological simulation, but the ionised gas mass accounts for less than 2${{\ \rm per\ cent}}$ of the predicted value. This suggests that either the bulk of the outflowing mass is in other gaseous phases or the current feedback models implemented in cosmological simulations need to be revised.
Tree Dimension and the Sauer-Shelah Dichotomy

We introduce tree dimension and its leveled variant in order to measure the complexity of leaf sets in binary trees. We then provide a tight upper bound on the size of such sets using leveled tree dimension. This, in turn, implies both the famous Sauer-Shelah Lemma for VC dimension and Bhaskar's version for Littlestone dimension, giving clearer insight into why these results place the exact same upper bound on their respective shatter functions. We also classify the isomorphism types of maximal leaf sets by tree dimension. Finally, we generalize this analysis to higher-arity trees.
Limits of Mahler measures in multiple variables

We prove that certain sequences of Laurent polynomials, obtained from a fixed Laurent polynomial P by monomial substitutions, give rise to sequences of Mahler measures which converge to the Mahler measure of P. This generalizes previous work of Boyd and Lawton, who considered univariate monomial substitutions. We provide moreover an explicit upper bound for the error term in this convergence, generalizing work of Dimitrov and Habegger, and a full asymptotic expansion for a family of 2-variable polynomials, whose Mahler measures were studied independently by the third author.
Effort Dynamics with Selective Predator Harvesting of a Ratio-dependent Predator-Prey System

Sustainable harvesting of renewable resources is a core issue in fisheries and forest management. The over-exploitation of valuable resources has led to undesirable extinction of several biological species causing non-reversible damage to ecology and environment. In this paper, the selective predator harvesting is considered in a predator prey system. The logistic growth of prey and ratio-dependent functional response is assumed. The dynamically varying effort is employed for selective harvesting of predator species using Michaelis-Menten type function The boundedness, positivity and persistence of the nonlinear dynamical system is established. The system being non-singular about origin, its dynamics is explored by transforming it to a regular system. It is observed that the system may collapse even with positive initial conditions attracted to one of the boundary points under certain conditions. The local stability of various other equilibrium states of the dynamic model is investigated. The region of attraction of interior equilibrium state is obtained using Lyapunov stability. The occurrence of hopf bifurcation about the coexistence equilibrium point with respect to the parameter m (fraction of predators available for harvesting) is established. The sustainable harvesting is possible in two ways firstly in the form of stable interior equilibrium state and secondly in the form of limit cycle. Considering the fraction m as a control variable, the optimal harvesting policy is obtained using Pontraygin’s maximum principle. The two-parameter bifurcation diagram is obtained with respect to c and d (the cost of harvesting and the death rate of predators respectively). The two bi-stability regions of parameter space are identified. One of the regions shows bi-stability of the origin and the effort free equilibrium state. The other region involves the bi-stability of the origin and interior equilibrium state. It is proved that for sustainable harvesting of predators the ratio of p to c (price to cost) should be greater than a certain threshold value dependent on predator density. The analytical results are illustrated numerically with different parametric values.
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach

Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time. Offline (explicit) solutions for MPC attempt to alleviate real time computational challenges using either multiparametric programming or machine learning. The multiparametric approaches are typically applied to linear or quadratic MPC problems, while learning-based approaches can be more flexible and are less memory-intensive. Existing learning-based approaches offer significant speedups, but the challenge becomes ensuring constraint satisfaction while maintaining good performance. In this paper, we provide a neural network parameterization of MPC policies that explicitly encodes the constraints of the problem. By exploring the interior of the MPC feasible set in an unsupervised learning paradigm, the neural network finds better policies faster than projectionbased methods and exhibits substantially shorter solve times. We use the proposed policy to solve a robust MPC problem, and demonstrate the performance and computational gains on a standard test system.
A Tikhonov approach to level set curvature computation

In numerical simulations of two-phase flows, the computation of the curvature of the interface is a crucial ingredient. Using a finite element and level set discretization, the discrete interface is typically the level set of a low order polynomial, which often results in a poor approximation of the interface curvature. We present an approach to curvature computation using an approximate inversion of the $L^2$ projection operator from the Sobolev space $H^2$ or $H^3$. For finite element computation of the approximate inverse, the resulting higher order equation is reformulated as a system of second order equations. Due to the Tikhonov regularization, the method is demonstrated to be stable against discretization irregularities. Numerical examples are shown for interior interfaces as well as interfaces intersecting the boundary of the domain.
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis

Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e. to predict the emotion category of a dialog as a whole. More importantly, these models cannot benefit from the context provided by the whole dialog. In real-world applications, annotations to dialog could fine-grained, including both utterance-level tags (e.g. speaker type, intent category, and emotion category), and dialog-level tags (e.g. user satisfaction, and emotion curve category). In this paper, we propose a Context-based Hierarchical Attention Capsule~(Chat-Capsule) model, which models both utterance-level and dialog-level emotions and their interrelations. On a dialog dataset collected from customer support of an e-commerce platform, our model is also able to predict user satisfaction and emotion curve category. Emotion curve refers to the change of emotions along the development of a conversation. Experiments show that the proposed Chat-Capsule outperform state-of-the-art baselines on both benchmark dataset and proprietary dataset. Source code will be released upon acceptance.
Precise and continuous measurement of plant heights in an agricultural field using a time-lapse camera

Plant height is an important trait linked to yield potential and phenotyping; hence, a method for measuring plant height with sufficient temporal and spatial resolutions is required. Furthermore, a low-cost and easy-to-use method is desirable for practical application in agricultural fields. In previous studies, methods for measuring plant height have only fulfilled one or a few of these requirements, and there have been very few studies that have attempted to fulfill all the above requirements in one method. The current study proposes a low-cost plant height measurement system using a commercial time-lapse camera to capture seasonal and year-to-year variations in plant height that represent plant heights at the site scale. The system’s performance was tested in a rice field in Japan for two growing seasons. Plant heights were determined to the nearest 1-cm in height from the camera images by referencing a scale bar that was erected next to the four target plants. The obtained plant heights were corrected for vertical distortion (referred to as the ‘displacement effect’ in the main text), and those were compared with the direct height measurements for each the target plant and site-scaled plant heights that were averaged from 10 samples. For both cases, good agreement between the captured and referenced plant heights was obtained. The system was able to reflect detailed seasonal variations in the plant heights including the maximum heights and the times at which they occurred as well as year-to-year differences. Consequently, the developed system appears to fulfill all the necessary requirements for practical measurement of plant height on-site in agricultural fields.
THE EFFECT OF WATCHING AUTHENTIC VIDEOS ON IMPROVEMENT OF IRANIAN EFL LEARNERS ' LISTENING COMPREHENSION ABILITY

In this study the effect of frequent exposure to authentic visual materials on Iranian EFL learners' listening comprehension ability was investigated. In order to conduct the study, 60 Iranian intermediate students were selected out of a pool of 300, based on their result in a placement test. The selected participants were randomly assigned into two groups of experimental and control, each containing 30 participants. The listening ability of the participants was tested by a pretest before starting of the treatment and no significant difference was observed. After a period of two months, in which the experimental group received treatment of exposure to authentic visual material, a posttest was administrated and the scores obtained from two groups were compared using a t-test. It was observed that the experimental group that received the treatment achieved significantly better scores. It was concluded that frequent exposure to authentic videos has a positive impact on improvement of learners’ listening ability.
On Last-Iterate Convergence Beyond Zero-Sum Games

Most existing results about \emph{last-iterate convergence} of learning dynamics are limited to two-player zero-sum games, and only apply under rigid assumptions about what dynamics the players follow. In this paper we provide new results and techniques that apply to broader families of games and learning dynamics. First, we use a regret-based analysis to show that in a class of games that includes constant-sum polymatrix and strategically zero-sum games, dynamics such as \emph{optimistic mirror descent (OMD)} have \emph{bounded second-order path lengths}, a property which holds even when players employ different algorithms and prediction mechanisms. This enables us to obtain $O(1/\sqrt{T})$ rates and optimal $O(1)$ regret bounds. Our analysis also reveals a surprising property: OMD either reaches arbitrarily close to a Nash equilibrium, or it outperforms the \emph{robust price of anarchy} in efficiency. Moreover, for potential games we establish convergence to an $\epsilon$-equilibrium after $O(1/\epsilon^2)$ iterations for mirror descent under a broad class of regularizers, as well as optimal $O(1)$ regret bounds for OMD variants. Our framework also extends to near-potential games, and unifies known analyses for distributed learning in Fisher's market model. Finally, we analyze the convergence, efficiency, and robustness of \emph{optimistic gradient descent (OGD)} in general-sum continuous games.
ECO v1: Towards Event-Centric Opinion Mining

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.
A Framework for Fast Polarity Labelling of Massive Data Streams

Many of the existing sentiment analysis techniques are based on supervised learning, and they demand the availability of valuable training datasets to train their models. When dataset freshness is critical, the annotating of high speed unlabelled data streams becomes critical but remains an open problem. In this paper, we propose PLStream , a novel Apache Flink-based framework for fast polarity labelling of massive data streams, like Twitter tweets or online product reviews. We address the associated implementation challenges and propose a list of techniques including both algorithmic improvements and system optimizations. A thorough empirical validation with two real-world workloads demonstrates that PLStream is able to generate high quality labels (almost 80% accuracy) in the presence of high-speed continuous unlabelled data streams (almost 16 , 000 tuples/sec) without any manual efforts.
An 8.56 keV Absorption Line in the Hyperluminous X-Ray Source in NGC 4045: Ultrafast Outflow or Cyclotron Line?

We report on the discovery of an absorption line at E=8.56−0.11+0.05 keV detected with a significance of >3. 3σ in the NuSTAR and XMM-Newton spectra of a newly discovered hyperluminous X-ray source (L X > 1041 erg s−1) in the galaxy NGC 4045 at a distance of 32 Mpc. The source was first discovered serendipitously in a Swift/XRT observation of the galaxy, and Swift monitoring reveals a highly variable source changing by over an order of magnitude from maximum to minimum. The origin of the absorption line appears likely to be from highly ionized iron with a blueshift of 0.19c, indicating an ultrafast outflow. However, the large equivalent width of the line ( EW=−0.22−0.09+0.08 keV) paired with the lack of other absorption lines detected is difficult to reconcile with models. An alternative explanation is that the line is due to a cyclotron resonance scattering feature produced by the interaction of X-ray photons with the powerful magnetic field of a neutron star.
Linearization for difference equations with infinite delay

In this article, we construct a conjugacy map for a linear difference equation with infinite delay and corresponding nonlinear perturbation. We also prove that the conjugacy map is one one with some additional conditions. As an application of our result, we show that the cases of (uniform) exponential dichotomy follow from our result.
Improvement of knowledge and postural habits after an educational intervention program in school students

To study the effect of an educational intervention programme concerning knowledge and postural habits for back health on a group of 10 to 11-year-old students. Two groups of 5th grade primary school students were selected. A control group (CG) (n=16), and an experimental group (EG) (n=16) who developed a back-health educational programme. A follow-up was carried out one month after the end of the intervention. The proposed educational intervention improved the results of both the knowledge and postural habits of the participants. The increase in the level of knowledge concerning health and back care in the daily lives of the EG (x=6.32) was significantly greater than that observed in the CG (x=3.86), with a high effect size (U = 29.500; Z = -3.717; p <0.001, r = 0.66). In daily postural habits, the EG (x= 88.38) also increased their score significantly (U= 7.50; Z = -4.55; p <0.001, r = 0.80) compared to the CG (x= 74.88). In both knowledge and postural habits, the scores increased in the intervention. The educational intervention programme studied improves both knowledge and postural habits for back health of the 5th grade students participating in the study. The Physical Education class seems to be an ideal environment to develop back health educational programmes.