Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts on Native Communities

Competition between birds can influence species’ access to resources, altering species persistence within an environment and ultimately community structure. However, predicting where and when invasive species significantly compete with native species can be challenging due to a lack of information on interspecies interactions around resources in different environments. The extent to which critical resources, such as habitat space, territories, nesting sites and food, vary across different environments changes levels of competition and makes the importance of invasive–native species competition context specific. In order to better understand how competition between invasive and native species impacts invaded communities, it is important to identify critical resources, the species interacting over the resource and the functional traits that influence interaction frequency and outcome. Such an approach will allow a more mechanistic understanding of competition at the community level and facilitate better predictions of invasive bird impacts.
Connecting degree and polarity: An artificial language learning study

We investigate a new linguistic generalization in pre-trained language models (taking BERT (Devlin et al., 2019) as a case study). We focus on degree modifiers (expressions like slightly, very, rather, extremely) and test the hypothesis that the degree expressed by a modifier (low, medium or high degree) is related to the modifier's sensitivity to sentence polarity (whether it shows preference for affirmative or negative sentences or neither). To probe this connection, we apply the Artificial Language Learning experimental paradigm from psycholinguistics to a neural language model. Our experimental results suggest that BERT generalizes in line with existing linguistic observations that relate degree semantics to polarity sensitivity, including the main one: low degree semantics is associated with preference towards positive polarity.
Almost triangular Markov chains on N

A transition matrix $[U_{i,j}]_{i,j\geq 0}$ on $\mathbb{N}$ is said to be almost upper triangular if $U_{i,j}\geq 0\Rightarrow j\geq i-1$, so that the increments of the corresponding Markov chains are at least $-1$; a transition matrix $[L_{i,j}]_{i,j\geq 0}$ is said to be almost lower triangular if $L_{i,j}\geq 0\Rightarrow j\leq i+1$, and then, the increments of the corresponding Markov chains are at most $+1$. In the present paper, we characterize the recurrence, positive recurrence and invariant distribution for the class of almost triangular transition matrices. The upper case appears to be the simplest in many ways, with existence and uniqueness of invariant measures, when in the lower case, existence as well as uniqueness are not guaranteed. We present the time-reversal connection between upper and lower almost triangular transition matrices, which provides classes of integrable lower triangular transition matrices. These results encompass the case of birth and death processes (BDP) that are famous Markov chains (or processes) taking their values in $\mathbb{N}$, which are simultaneously almost upper and almost lower triangular, and whose study has been initiated by Karlin&McGregor in the 1950's. They found invariant measures, criteria for recurrence, null recurrence, among others; their approach relies on some profound connections they discovered between the theory of BDP, the spectral properties of their transition matrices, the moment problem, and the theory of orthogonal polynomials. Our approach is mainly combinatorial and uses elementary algebraic methods; it is somehow more direct and does not use the same tools.
MMCoVaR: multimodal COVID-19 vaccine focused data repository for fake news detection and a baseline architecture for classification

The outbreak of COVID-19 has resulted in an "infodemic" that has encouraged the propagation of misinformation about COVID-19 and cure methods which, in turn, could negatively affect the adoption of recommended public health measures in the larger population. In this paper, we provide a new multimodal (consisting of images, text and temporal information) labeled dataset containing news articles and tweets on the COVID-19 vaccine. We collected 2,593 news articles from 80 publishers for one year between Feb 16th 2020 to May 8th 2021 and 24184 Twitter posts (collected between April 17th 2021 to May 8th 2021). We combine ratings from two news media ranking sites: Medias Bias Chart and Media Bias/Fact Check (MBFC) to classify the news dataset into two levels of credibility: reliable and unreliable. The combination of two filters allows for higher precision of labeling. We also propose a stance detection mechanism to annotate tweets into three levels of credibility: reliable, unreliable and inconclusive. We provide several statistics as well as other analytics like, publisher distribution, publication date distribution, topic analysis, etc. We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset. We find that the proposed architecture has an F-Score of 0.919 and accuracy of 0.882 for fake news detection. Furthermore, we provide benchmark performance for misinformation detection on tweet dataset. This new multimodal dataset can be used in research on COVID-19 vaccine, including misinformation detection, influence of fake COVID-19 vaccine information, etc.
Conditioning Markov processes: a Lagrangian and Hamiltonian perspective

When analysing statistical systems or stochastic processes, it is often interesting to ask how they behave given that some observable takes some prescribed value. This conditioning problem is well understood within the linear operator formalism based on rate matrices or Fokker-Planck operators, which describes the dynamics of many independent random walkers. Relying on certain spectral properties of the biased linear operators, guaranteed by the Perron-Frobenius theorem, an effective process can be found such that its path probability is equivalent to the conditional path probability. In this paper, we extend those results for nonlinear Markov processes that appear when the many random walkers are no longer independent, and which can be described naturally through a Lagrangian–Hamiltonian formalism within the theory of large deviations. We identify the appropriate spectral problem as being a Hamilton-Jacobi equation for a biased Hamiltonian, for which we conjecture that two special global solutions exist, replacing the Perron-Frobenius theorem concerning the positivity of the dominant eigenvector. We then devise a rectification procedure based on a canonical gauge transformation of the biased Hamiltonian, yielding an effective dynamics in agreement with the original conditioning. Along the way, we present simple examples in support of our conjecture, we examine its consequences on important physical objects such as the fluctuation symmetries of the biased and rectified processes as well as the dual dynamics obtained through time-reversal. We apply all those results to simple independent and interacting models, including a stochastic chemical reaction network and a population process called the Brownian Donkey.
Exploring Personality and Online Social Engagement: An Investigation of MBTI Users on Twitter

Text-based personality prediction by computational models is an emerging field with the potential to significantly improve on key weaknesses of survey-based personality assessment. We investigate 3848 profiles from Twitter with self-labeled Myers-Briggs personality traits (MBTI) – a framework closely related to the Five Factor Model of personality – to better understand how text-based digital traces from social engagement online can be used to predict user personality traits. We leverage BERT, a state-of-the-art NLP architecture based on deeplearning, to analyze various sources of text that hold most predictive power for our task. We find that biographies, statuses, and liked tweets contain significant predictive power for all dimensions of the MBTI system. We discuss our findings and their implications for the validity of the MBTI and the lexical hypothesis, a foundational theory underlying the Five Factor Model that links language use and behavior. Our results hold optimistic implications for personality psychologists, computational linguists, and other social scientists aiming to predict personality from observational text data and explore the links between language and core behavioral traits.
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate

Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs. In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality of the target distribution. AligNART divides the machine translation task into (i) alignment estimation and (ii) translation with aligned decoder inputs, guiding the decoder to focus on simplified one-to-one translation. To alleviate the alignment estimation problem, we further propose a novel alignment decomposition method. Our experiments show that AligNART outperforms previous non-iterative NART models that focus on explicit modality reduction on WMT14 En↔De and WMT16 Ro→En. Furthermore, AligNART achieves BLEU scores comparable to those of the state-of-the-art connectionist temporal classification based models on WMT14 En↔De. We also observe that AligNART effectively addresses the token repetition problem even without sequence-level knowledge distillation.
Optimal To-Do List Gamification for Long Term Planning

Most people struggle with prioritizing work. While inexact heuristics have been developed over time, there is still no tractable principled algorithm for deciding which of the many possible tasks one should tackle in any given day, month, week, or year. Additionally, some people suffer from cognitive biases such as the present bias, leading to prioritization of their immediate experience over long-term consequences which manifests itself as procrastination and inefficient task prioritization. Our method utilizes optimal gamification to help people overcome these problems by incentivizing each task by a number of points that convey how valuable it is in the long-run. We extend the previous version of our optimal gamification method with added services for helping people decide which tasks should and should not be done when there is not enough time to do everything. To improve the efficiency and scalability of the to-do list solver, we designed a hierarchical procedure that tackles the problem from the top-level goals to fine-grained tasks. We test the accuracy of the incentivised to-do list by comparing the performance of the strategy with the points computed exactly using Value Iteration for a variety of case studies. These case studies were specifically designed to cover the corner cases to get an accurate judge of performance. Our method yielded the same performance as the exact method for all case studies. To demonstrate its functionality, we released an API that makes it easy to deploy our method in Web and app services. We assessed the scalability of our method by applying it to to-do lists with increasingly larger numbers of goals, sub-goals per goal, hierarchically nested levels of subgoals. We found that the method provided through our API is able to tackle fairly large to-do lists having a 576 tasks. This indicates that our method is suitable for real-world applications.
Non-Markovian stochastic Schrödinger equation: Matrix-product-state approach to the hierarchy of pure states

We derive a stochastic hierarchy of matrix product states (HOMPS) for non-Markovian dynamics in open quantum system at finite temperature, which is numerically exact and efficient. HOMPS is obtained from the recently developed stochastic hierarchy of pure states (HOPS) by expressing HOPS in terms of formal creation and annihilation operators. The resulting stochastic first order differential equation is then formulated in terms of matrix product states and matrix product operators. In this way the exponential complexity of HOPS can be reduced to scale polynomial with the number of particles. The validity and efficiency of HOMPS is demonstrated for the spin-boson model and long chains where each site is coupled to a structured, strongly non-Markovian environment.
Graph Algorithms for Multiparallel Word Alignment

With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in the decoding of machine translation systems. At the same time, massively multilingual processing is becoming an important NLP scenario, and pretrained language and machine translation models that are truly multilingual are proposed. However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel. In this work, we exploit the multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph. We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction. Our experimental results show absolute improvements in F1 of up to 28% over the baseline bilingual word aligner in different datasets.
Diffeomorphic Image Registration with An Optimal Control Relaxation and Its Implementation

Image registration has played an important role in image processing problems, especially in medical imaging applications. It is well known that when the deformation is large, many variational models cannot ensure diffeomorphism. In this paper, we propose a new registration model based on an optimal control relaxation constraint for large deformation images, which can theoretically guarantee that the registration mapping is diffeomorphic. We present an analysis of optimal control relaxation for indirectly seeking the diffeomorphic transformation of Jacobian determinant equation and its registration applications, including the construction of diffeomorphic transformation as a special space. We also provide an existence result for the control increment optimization problem in the proposed diffeomorphic image registration model with an optimal control relaxation. Furthermore, a fast iterative scheme based on the augmented Lagrangian multipliers method (ALMM) is analyzed to solve the control increment optimization problem, and a convergence analysis is followed. Finally, a grid unfolding indicator is given, and a robust solving algorithm for using the deformation correction and backtrack strategy is proposed to guarantee that the solution is diffeomorphic. Numerical experiments show that the registration model we proposed can not only get a diffeomorphic mapping when the deformation is large, but also achieves the state-of-the-art performance in quantitative evaluations in comparing with other classical models.
An MRC Framework for Semantic Role Labeling

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work.
Formation and Stability of Various Type of Central Systems in Universe

In this particular article I will prove some former terminologies which were established in my two former papers about the Bodies and the Broken Parts and how these are basic components of several type of central systems on different geometrical scales in Universe. I will also prove that how these types of processes are required in the stability of these central systems. In this particular article I will prove n-type of central systems exists in universe in my formerly described N-time inflationary model of Universe.
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker

Pre-trained model such as BERT has been proved to be an effective tool for dealing with Information Retrieval (IR) problems. Due to its inspiring performance, it has been widely used to tackle with real-world IR problems such as document ranking. Recently, researchers have found that selecting"hard"rather than"random"negative samples would be beneficial for fine-tuning pre-trained models on ranking tasks. However, it remains elusive how to leverage hard negative samples in a principled way. To address the aforementioned issues, we propose a fine-tuning strategy for document ranking, namely Self-Involvement Ranker (SIR), to dynamically select hard negative samples to construct high-quality semantic space for training a high-quality ranking model. Specifically, SIR consists of sequential compressors implemented with pre-trained models. Front compressor selects hard negative samples for rear compressor. Moreover, SIR leverages supervisory signal to adaptively adjust semantic space of negative samples. Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance. SIR is a lightweight and general framework for pre-trained models, which simplifies the ranking process in industry practice. We test our proposed solution on MS MARCO with document ranking setting, and the results show that SIR can significantly improve the ranking performance of various pre-trained models. Moreover, our method became the new SOTA model anonymously on MS MARCO Document ranking leaderboard in May 2021.
Preparing for advanced practice radiation therapy in South Africa: Conceptual and curricular considerations

The higher education landscape in South Africa has recently changed with the new National Qualifications Framework, leading to the introduction of four-year degrees in all four radiography disciplines. Additionally, there have been developments in the extension of roles in radiography, while the need for interdisciplinary practice has also been emphasised. The Professional Board for Radiography and Clinical Technology (PBRCT) of the Health Professions Council of South Africa is currently revising the scope of the profession which will now include extended roles. However, the extended role concept in radiation therapy has not received the attention similar to Diagnostic/Medical Imaging. The aim of this paper is, therefore, to provide concept clarification and key considerations for developing a postgraduate curricular framework for training radiation therapists to practise in the envisaged extended roles. For this narrative review, a Boolean search for advanced practice and role extension in radiation therapy was done on all databases (43) available on EbscoHost to source for peer-reviewed articles published between January 1950 and September 2020. A total of 17 articles met the inclusion criteria and were used to frame the discussion. Advanced practice emerged as the more suitable concept as it goes beyond an extension of tasks to include critical characteristics that are necessary to drive transformation in the local social, professional and educational arenas. It is envisaged that some of the key points discussed in this paper could assist the PBRCT in thinking about the implementation of the proposed extended roles for radiation therapists in South Africa.
Wrapping trust for interoperability. A study of wrapped tokens

Blockchains are traditionally blind to the real world. This implies reliance on third parties called oracles when extrinsic data are needed for smart contracts. Oracle implementation, however, is still controversial and debated due to the reintroduction of trust and a single point of failure. The blindness to the real world also makes blockchains unable to communicate with each other, preventing any form of interoperability. This limitation prevents, for example, liquidity held in Bitcoin from flowing into DeFi applications. An early approach to the interoperability issue is constituted by “wrapped tokens”, representing blockchain native tokens issued on a non-native blockchain. Similar to how oracles reintroduce trust and a single point of failure, the issuance of wrapped tokens involves third parties whose characteristics need to be considered when evaluating the advantages of “crossing-chains”. This paper provides an overview of the available wrapped tokens and the main issuing procedures. Benefits, limitations, and implications for trust are listed and discussed.
Split Invariant Curves in Rotating Bar Potentials

Invariant curves are generally closed curves in Poincaré's surface of section. Here we study an interesting dynamical phenomenon, first discovered by Binney et al. in a rotating Kepler potential, where an invariant curve of the surface of section can split into two disconnected line segments under certain conditions, which is distinctively different from the islands of resonant orbits. We first demonstrate the existence of split invariant curves in the Freeman bar model, where all orbits can be described analytically. We find that the split phenomenon occurs when orbits are nearly tangent to the minor/major axis of the bar potential. Moreover, the split phenomenon seems “necessary” to avoid invariant curves intersecting with each other. Such a phenomenon appears only in rotating potentials, and we demonstrate its universal existence in other general rotating bar potentials. It also implies that actions are no longer proportional to the area bounded by an invariant curve if the split occurs, but they can still be computed by other means.
What Are the Attackers Doing Now? Automating Cyberthreat Intelligence Extraction from Text on Pace with the Changing Threat Landscape: A Survey

Cybersecurity researchers have contributed to the automated extraction of CTI from textual sources, such as threat reports and online articles describing cyberattack strategies, procedures, and tools. The goal of this article is to aid cybersecurity researchers in understanding the current techniques used for cyberthreat intelligence extraction from text through a survey of relevant studies in the literature. Our work finds 11 types of extraction purposes and 7 types of textual sources for CTI extraction. We observe the technical challenges associated with obtaining available clean and labeled data for replication, validation, and further extension of the studies. We advocate for building upon the current CTI extraction work to help cybersecurity practitioners with proactive decision-making such as in threat prioritization and mitigation strategy formulation to utilize knowledge from past cybersecurity incidents.