Miracle-dz/UniFAR-FTUs huggingface dataset:
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks

Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottom-up parsing to tackle both tasks. The key idea is based on the observation that if we traverse a constituency tree in post-order, i.e., visiting a parent after its children, then two consecutively visited spans would share a boundary. Our model tracks the shared boundaries and predicts the next boundary at each step by leveraging a pointer network. As a result, it needs only linear steps to parse and thus is efficient. It also maintains a parsing configuration for structural consistency, i.e., always outputting valid trees. Experimentally, our model achieves the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and it also achieves strong performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA. Our code will be available at https://github.com/xxxxx.
Fair Regression under Sample Selection Bias

Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the regression setting. However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions. This assumption is often violated in real world due to the sample selection bias between the training and testing data. In this paper, we develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process. Our framework adopts the classic Heckman model for bias correction and the Lagrange duality to achieve fairness in regression based on a variety of fairness notions. Heckman model describes the sample selection process and uses a derived variable called the Inverse Mills Ratio (IMR) to correct sample selection bias. We use fairness inequality and equality constraints to describe a variety of fairness notions and apply the Lagrange duality theory to transform the primal problem into the dual convex optimization. For the two popular fairness notions, mean difference and mean squared error difference, we derive explicit formulas without iterative optimization, and for Pearson correlation, we derive its conditions of achieving strong duality. We conduct experiments on three real-world datasets and the experimental results demonstrate the approach’s effectiveness in terms of both utility and fairness metrics.
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to first identify and then mimic optimal decision-making strategies. Our work evaluates this method's ability to scale to vast datasets consisting almost entirely of sub-optimal noise. A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets. We re-purpose prioritized experience sampling to locate expert-level demonstrations among millions of low-performance samples. This modification enables offline agents to learn state-of-the-art policies in benchmark tasks using datasets where expert actions are outnumbered nearly 65:1.
Sliced Mutual Information: A Scalable Measure of Statistical Dependence

Mutual information (MI) is a fundamental measure of statistical dependence, with a myriad of applications to information theory, statistics, and machine learning. While it possesses many desirable structural properties, the estimation of high-dimensional MI from samples suffers from the curse of dimensionality. Motivated by statistical scalability to high dimensions, this paper proposes sliced MI (SMI) as a surrogate measure of dependence. SMI is defined as an average of MI terms between one-dimensional random projections. We show that it preserves many of the structural properties of classic MI, while gaining scalable computation and efficient estimation from samples. Furthermore, and in contrast to classic MI, SMI can grow as a result of deterministic transformations. This enables leveraging SMI for feature extraction by optimizing it over processing functions of raw data to identify useful representations thereof. Our theory is supported by numerical studies of independence testing and feature extraction, which demonstrate the potential gains SMI offers over classic MI for high-dimensional inference.
Explainable Fact-Checking Through Question Answering

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps — this separation aids models’ explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities1.
Configuration entropy and stability of bottomonium radial excitations in a plasma with magnetic fields

Heavy vector mesons produced in a heavy ion collision are important sources of information about the quark gluon plasma (QGP). For instance, the fraction of bottomonium states observed in such a collision is altered by the dissociation eﬀect caused by the plasma. So, it is very important to understand how the properties of the plasma, like temperature ( T ), density and the presence of background magnetic ﬁelds ( eB ), aﬀect the dissociation of bottomonium in the thermal medium. AdS/QCD holographic models provide a tool for investigating the properties of heavy mesons inside a thermal medium. The meson states are represented by quasinormal modes in a black hole geometry. In this work we calculate the quasinormal modes and the associated complex frequencies for the four lowest levels of radial excitation of bottomonium inside a plasma with a magnetic ﬁeld background. We also calculate the diﬀerential conﬁguration entropy (DCE) for all these states and investigate how the dissociation eﬀect produced by the magnetic ﬁeld is translated into a dependence of the DCE on eB . increases the excitation is that the energy density associated with the bottomonium quasinormal modes presents a singularity near the black hole horizon for some combination of values of T, eB and n . As we show here, it is possible to separate the singular factor and deﬁne a square integrable quantity that provides a DCE that is always ﬁnite. In addition, we discovered that, working with the potentially singular energy density, one ﬁnds a very interesting to use the DCE as a tool for determining the dissociation temperature of the meson quasisates.
Recurrent Model-Free RL is a Strong Baseline for Many POMDPs

Many problems in RL, such as meta RL, robust RL, and generalization in RL, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques in their respective domains. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs.1
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency

The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k. a. visual explanation, to interpret convolutional neural networks. Compared to iteration based saliency methods, single backward pass based saliency methods benefit from faster speed, and they are widely used in downstream visual tasks. Thus, we focus on single backward pass based methods. However, existing methods in this category struggle to successfully produce fine-grained saliency maps concentrating on specific target classes. That said, producing faithful saliency maps satisfying both target-selectiveness and fine-grainedness using a single backward pass is a challenging problem in the field. To mitigate this problem, we revisit the gradient flow inside the network, and find that the entangled semantics and original weights may disturb the propagation of target-relevant saliency. Inspired by those observations, we propose a novel visual saliency method, termed Target-Selective Gradient Backprop (TSGB), which leverages rectification operations to effectively emphasize target classes and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps. The proposed TSGB consists of two components, namely, TSGB-Conv and TSGB-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively. Extensive qualitative and quantitative experiments on the ImageNet and Pascal VOC datasets show that the proposed method achieves more accurate and reliable results than the other competitive methods. Code is available at https://github.com/123fxdx/CNNvisualizationTSGB
Complex Network-Based Approach for Feature Extraction and Classification of Musical Genres

Musical genre's classification has been a relevant research topic. The association between music and genres is fundamental for the media industry, which manages musical recommendation systems, and for music streaming services, which may appear classified by genres. In this context, this work presents a feature extraction method for the automatic classification of musical genres, based on complex networks and their topological measurements. The proposed method initially converts the musics into sequences of musical notes and then maps the sequences as complex networks. Topological measurements are extracted to characterize the network topology, which composes a feature vector that applies to the classification of musical genres. The method was evaluated in the classification of 10 musical genres by adopting the GTZAN dataset and 8 musical genres by adopting the FMA dataset. The results were compared with methods in the literature. The proposed method outperformed all compared methods by presenting high accuracy and low standard deviation, showing its suitability for the musical genre's classification, which contributes to the media industry in the automatic classification with assertiveness and robustness. The proposed method is implemented in an open source in the Python language and freely available at https://github.com/omatheuspimenta/examinner.
Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations

NLP models that process multiple texts often struggle in recognizing corresponding and salient information that is often differently phrased, and consolidating the redundancies across texts. To facilitate research of such challenges, the sentence fusion task was proposed, yet previous datasets for this task were very limited in their size and scope. In this paper, we revisit and substantially extend previous dataset creation efforts. With careful modifications, relabeling, and employing complementing data sources, we were able to more than triple the size of a notable earlier dataset.Moreover, we show that our extended version uses more representative texts for multi-document tasks and provides a more diverse training set, which substantially improves model performance.
A computational approach to the Kiefer-Weiss problem for sampling from a Bernoulli population

Abstract We present a computational approach to the solution of the Kiefer-Weiss problem. Algorithms for construction of the optimal sampling plans and evaluation of their performance are proposed. In the particular case of Bernoulli observations, the proposed algorithms are implemented in the form of R program code. Using the developed computer program, we numerically compare the optimal tests with the respective sequential probability ratio test (SPRT) and the fixed sample size test for a wide range of hypothesized values and type I and type II errors. The results are compared with those of D. Freeman and L. Weiss (Journal of the American Statistical Association, 59, 1964). The R source code for the algorithms of construction of optimal sampling plans and evaluation of their characteristics is available at https://github.com/tosinabase/Kiefer-Weiss.
Research on Knowledge based Expert System for Medical Diagnosis

In this paper we propose the design and implementation of a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms and signs. To diagnosis diseases, user will be asked by the system for diﬀerent questions and ﬁnally inference engine will use certainty factor to prune out low possible solutions. In this system some important aspects like Knowledge bases system, Knowledge representation, Inference Engine has been addressed. New certainty fact has been introduced to get conclusion about same ﬁring rules. The proposed disease diagnosis system also uses a graphical user user interface to facilitate user to interact a with expert system more easily. The proposed system is generic and knowledge based, and it can be integrated with any rule bases system in disease diagnosis.
TEET! Tunisian Dataset for Toxic Speech Detection

The complete freedom of expression in social media has its costs especially in spreading harmful and abusive content that may induce people to act accordingly. Therefore, the need of detecting automatically such a content becomes an urgent task that will help and enhance the efficiency in limiting this toxic spread. Compared to other Arabic dialects which are mostly based on MSA, the Tunisian dialect is a combination of many other languages like MSA, Tamazight, Italian and French. Because of its rich language, dealing with NLP problems can be challenging due to the lack of large annotated datasets. In our context of detecting hate and abusive speech for tunisian dialect, the only existing annotated dataset is T-HSAB combining 6,039 annotated comments as hateful, abusive or normal. In this paper we are introducing a larger annotated dataset composed of approximately 10k of comments. We provide an in-depth exploration of its vocabulary as well as the results of the classification performance of machine learning classifiers like NB and SVM and deep learning models such as ARBERT, MARBERT and XLM-R.
Oscillations and Mass Draining that Lead to a Sympathetic Eruption of a Quiescent Filament

In this paper, we present a multiwavelength analysis to mass draining and oscillations in a large quiescent filament prior to its successful eruption on 2015 April 28. The eruption of a smaller filament that was parallel and in close, ∼350″ proximity was observed to induce longitudinal oscillations and enhance mass draining within the filament of interest. The longitudinal oscillation with an amplitude of ∼25 Mm and ∼23 km s−1 underwent no damping during its observable cycle. Subsequently the slightly enhanced draining may have excited a eruption behind the limb, leading to a feedback that further enhanced the draining and induced simultaneous oscillations within the filament of interest. We find significant damping for these simultaneous oscillations, where the transverse oscillations proceeded with the amplitudes of ∼15 Mm and ∼14 km s−1, while the longitudinal oscillations involved a larger displacement and velocity amplitude (∼57 Mm, ∼43 km s−1). The second grouping of oscillations lasted for ∼2 cycles and had a similar period of ∼2 hr. From this, the curvature radius and transverse magnetic field strength of the magnetic dips supporting the filaments can be estimated to be ∼355 Mm and ≥34 G. The mass draining within the filament of interest lasted for ∼14 hr. The apparent velocity grew from ∼35 to ∼85 km s−1, with the transition being coincident with the occurrence of the oscillations. We conclude that two filament eruptions are sympathetic, i.e., the eruption of the quiescent filament was triggered by the eruption of the nearby smaller filament.
Representation Learning for Online and Offline RL in Low-rank MDPs

This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL procedures such as exploration and exploitation, in a sample efficient manner. We focus on the low-rank Markov Decision Processes (MDPs) where the transition dynamics correspond to a low-rank transition matrix. Unlike prior works that assume the representation is known (e.g., linear MDPs), here we need to learn the representation for the low-rank MDP. We study both the online RL and offline RL settings. For the online setting, operating with the same computational oracles used in FLAMBE (Agarwal et.al), the state-of-art algorithm for learning representations in low-rank MDPs, we propose an algorithm REP-UCB Upper Confidence Bound driven Representation learning for RL), which significantly improves the sample complexity from $\widetilde{O}( A^9 d^7 / (\epsilon^{10} (1-\gamma)^{22}))$ for FLAMBE to $\widetilde{O}( A^2 d^4 / (\epsilon^2 (1-\gamma)^{5}) )$ with $d$ being the rank of the transition matrix (or dimension of the ground truth representation), $A$ being the number of actions, and $\gamma$ being the discounted factor. Notably, REP-UCB is simpler than FLAMBE, as it directly balances the interplay between representation learning, exploration, and exploitation, while FLAMBE is an explore-then-commit style approach and has to perform reward-free exploration step-by-step forward in time. For the offline RL setting, we develop an algorithm that leverages pessimism to learn under a partial coverage condition: our algorithm is able to compete against any policy as long as it is covered by the offline distribution.
Ghost of vector fields in compact stars

Spontaneous scalarization is a mechanism that allows a scalar field to go undetected in weak gravity environments and yet develop a nontrivial configuration in strongly gravitating systems. At the perturbative level it manifests as a tachyonic instability around spacetimes that solve Einstein's equations. The endpoint of this instability is a nontrivial scalar field configuration that can significantly modify a compact object's structure and can produce observational signatures of the scalar field's presence. Does such a mechanism exists for vector fields? Here we revisit the model that constitutes the most straightforward generalization of the original scalarization model to a vector field and perform a perturbative analysis. We show that a ghost appears as soon as the square of the naive effective mass squared becomes negative anywhere. This result poses a serious obstacle in generalizing spontaneous scalarization to vector fields.
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?

Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like software engineering. In this article, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers’ e-mails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in software engineering by replacing the personality detection tool used to infer developers’ personalities from pull-request discussions and e-mails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain.
Analysis of a nonlinear necrotic tumor model with angiogenesis and a periodic supply of external nutrients

In this paper, we consider a free boundary problem modeling the growth of spherically symmetric necrotic tumors with angiogenesis and a ω-periodic supply φ(t) of external nutrients. In the model, the consumption rate of the nutrient and the proliferation rate of tumor cells S(σ) are both general nonlinear functions. The well-posedness and asymptotic behavior of solutions are studied. We show that if the average of S(φ(t)) is nonpositive, then all evolutionary tumors will finally vanish; the converse is also ture. If instead the average of S(φ(t)) is positive, then there exists a unique positive periodic solution and all other evolutionary tumors will converge to this periodic state.
Zero-CPU Collection with Direct Telemetry Access

Programmable switches are driving a massive increase in fine-grained measurements. This puts significant pressure on telemetry collectors that have to process reports from many switches. Past research acknowledged this problem by either improving collectors' stack performance or by limiting the amount of data sent from switches. In this paper, we take a different and radical approach: switches are responsible for directly inserting queryable telemetry data into the collectors' memory, bypassing their CPU, and thereby improving their collection scalability. We propose to use a method we call direct telemetry access, where switches jointly write telemetry reports directly into the same collector's memory region, without coordination. Our solution, DART, is probabilistic, trading memory redundancy and query success probability for CPU resources at collectors. We prototype DART using commodity hardware such as P4 switches and RDMA NICs and show that we get high query success rates with a reasonable memory overhead. For example, we can collect INT path tracing information on a fat tree topology without a collector's CPU involvement while achieving 99.9% query success probability and using just 300 bytes per flow.