Miracle-dz/UniFAR-FTUs huggingface dataset:
Into the bibliography jungle: using random forests to predict dissertations' reference section

Cited-works-lists in Humanities dissertations are typically the result of five years of work. However, despite the long-standing tradition of reference mining, no research has systematically untapped the bibliographic data of existing electronic thesis collections. One of the main reasons for this is the difficulty of creating a tagged gold standard for the around 300 pages long theses. In this short paper, we propose a page-based random forest (RF) prediction approach which uses a new corpus of Literary Studies Dissertations from Germany. Moreover, we will explain the handcrafted but computationally informed feature-selection process. The evaluation demonstrates that this method achieves an F1 score of 0.88 on this new dataset. In addition, it has the advantage of being derived from an interpretable model, where feature relevance for prediction is clear, and incorporates a simplified annotation process.
A Game with Complex Rules: Literature References in Literary Studies

Existing systems for reference extraction and segmentation are mostly tailored towards STEM fields (science, technology, engineering, medicine) and social sciences and can not properly handle references in literary studies. We present our annotation guidelines for literature references in literary studies and give an overview of difficult cases we encountered when creating a corpus of annotated scholarly works for literary studies. Specifically, we present challenges and requirements we identified for reference extraction and segmentation from scholarly articles in the field of literary studies.
Abduction in (Probabilistic) Answer Set Programming

Answer Set Programming (ASP) is a branch of Logic Programming particularly useful for representing complex domains. Logic abduction, the reasoning strategy that deals with incomplete data, is tightly related to ASP, and encodes incompleteness through abducibles. The goal of logic abduction is to find the minimal set of abducibles (where minimality is usually considered in terms of set inclusion) that explains a query. Recently, abductive reasoning has been introduced in the context of Probabilistic Logic Programming, but no solutions are available for Probabilistic Answer Set Programming (PASP). In this paper, we close this gap and propose an algorithm to perform abduction both in ASP and in PASP.
Introducing Building Blocks for Industry 4.0, an Analytics Application for the Federated EFPF Platform

The increment of data generated through the implementation of Industry4.0 standards has allowed the development and implementation of industrial analytical and predictive maintenance tools. However, the solutions are tailor-made for each use case, and the availability of generic tools to use in a large spectrum of applications is limited. This situation can change with the disruption of industrial cloud federated platforms allowing the connection of Internet-of-Things (IoT) devices to cloud-native applications that can enhance the data-driven decisions taken from analytical tools working with manufacturing data. In this paper, we propose an application with three analytic modules addressed for the manufacturing industry and developed to work as part of the EFPF ecosystem which will allow the use of data analytics and predictive maintenance methodologies for SMEs.
On the distribution of galaxy ellipticity in clusters

We study the distribution of projected ellipticity n( ) for galaxies in a sample of 20 rich (Richness > 2) nearby (z < 0:1) clusters of galaxies. We nd no evidence of dierences in n( ), although the nearest cluster in the sample (the Coma Cluster) is the largest outlier (P (same) < 0:05). We then study n( ) within the clusters, and nd that increases with projected cluster-centric radius R (hereafter the -R relation). This trend is preserved at xed magnitude, showing that this relation exists over and above the trend of more luminous galaxies to be both rounder and more common in the centres of clusters. The -R relation is particularly strong in the subsample of intrinsically attened galaxies (
Extracting literature references in German Speaking Geography - the GEOcite project

The paper outlines the motivation and build-up of the DFG-funded GEO cite project at University of Passau. The project works on a domain-specific approach to automatically extract, segment, match and visualize literature references in the German speaking geography domain with the objective to provide a novel basis for a scientometric monitoring instrument for the community. In this paper, we describe the GEO cite corpus, its construction and elaborate on a preliminary evaluation of different approaches to extract and segment references from the digitized part of the corpus. We further evaluate the EXCITE segmentation model [1] on different datasets of German research papers. The results of our evaluation show small improvements with domain-specific and increased training data.
Argumentation-based Causal and Counterfactual Reasoning

In this paper we present a model for argumentative causal and counterfactual reasoning in a logical setting. Causal knowledge is represented in this system using Pearl’s causal model of a set of structural equations and a set of assumptions expressed in propositional logic. Queries concerning observations or actions can be answered by constructing an argumentation framework and determining its extensions. For counterfactual queries we propose an argumentation-based implementation of the twin network method and analyse its expressiveness.
BeClone: Behavior Cloning with Inference for Real-Time Strategy Games

Behavior cloning (BC) techniques that combine self-play capabilities with imitation learning from experts to reﬁne self-play models have shown performance improvement in robotics simulation domains. In this paper, we investigate the performance of this technique on Real-time strategy game tasks. One challenge with this approach is the training time of agents and real-time adaptation to opponent strategies. We present a framework BeClone for training agents in two phases. The ﬁrst phase uses behavior cloning (BC) to learn base policies. The second phase uses an advantage actor-critic (A2C) reinforcement learning algorithm to adapt base strategies through self-play to explore the action space. We demonstrate the success of the BeClone framework on the microRTS domain through the comparison of the performance of the agents against the baseline A2C agent proposed by Huang and Ontanon. Our results on the resource gathering benchmark show improvement in agent performance both in terms of rewards and training time.
Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment

Cross-lingual alignment of word embeddings are important in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches relying on learning structure-preserving transformations, using adversarial networks and refinement strategies, suffer from instability and convergence issues. This paper proposes BioSpere, a novel multi-stage framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space, by combining adversarial initialization, refinement procedure and point set registration. Experiments for parallel dictionary induction and word similarity demonstrate state-of-the-art unsupervised results for BioSpere on diverse languages – showcasing robustness against variable adversarial performance.
Quantum critical behavior influenced by measurement backaction in ultracold gases

Recent realizations of quantum gas microscope offer the possibility of continuous monitoring of the dynamics of a quantum many-body system at the single-particle level. By analyzing effective non-Hermitian Hamiltonians of interacting bosons in an optical lattice and continuum, we demonstrate that the backaction of quantum measurement shifts the quantum critical point and gives rise to a unique critical phase beyond the terrain of the standard universality class. We perform mean-field and strong-coupling-expansion analyses and show that non-Hermitian contributions shift the superfluid--to-Mott-insulator transition point. Using a low-energy effective field theory, we discuss critical behavior of the one-dimensional interacting Bose gas subject to the measurement backaction. We derive an exact ground state of the effective non-Hermitian Hamiltonian and find a unique critical behavior beyond the Tomonaga-Luttinger liquid universality class. We propose experimental implementations of post-selections using quantum gas microscopes to simulate the non-Hermitian dynamics and argue that our results can be investigated with current experimental techniques in ultracold atoms.
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code

Recent works have widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of various Transformer-based language model architectures for source code. This work investigates another important aspect of such models, namely the effect of different subtokenization options, and aims at identifying most effective and length-efficient subtokenizations, taking into account code specifics. We propose subtokenziation that reduces average length by 17% without downstream performance drop, and show that a carefully chosen subtokenization may improve quality by 0.5-2%, possibly with some length increase.
Efficient estimate of Bayes factors from Reversible Jump output

We exend Meng and Wong (1996) identity from a fixed to a varying dimentional setting. The identity is a very powerful tool to estimate ratios of normalizing constants and thus can be used to evaluate Bayes factors. The extention is driven by the reversibler jump algorithm so that the output from the semplar can be directly used to efficiently estimate the required Bayes factor. Two applications, involving linear and logistic regression models, illustrate the advantages of the suggested approach with respect to alternatives previously proposed in the literature.
Approaches for Automated Data Quality Analysis: Syntactic and Semantic Assessment

: Data quality significantly influences data usability and plays an important role in data trading. This paper presents a data quality analysis (DQA) of data tables on two levels. The first, the so-called syntactic level, concerns the structure of the elements within the database and the second, the so-called semantic level, concerns the relationship between the elements in the database and the "real world". Based on a literature review the most relevant data quality criteria and corresponding metrics were derived. Subsequently, based on heuristics, a data-centric approach and an unsupervised machine learning clustering algorithm DBSCAN, a service for automated DQA, is designed and implemented (syntactic DQA). In the next step, an automated semantic DQA service as well. The approach is used to examine data tables for example for missing relevant columns (i.e., semantic completeness). A data quality index represents the service s’ output, which is derived from the automated analysis of various data quality criteria. This enables the assessment of data quality, as well as the detection of potentials for improving quality and thus increasing the value of tradeable data.
SHAPING THE BROWN DWARF DESERT: PREDICTING THE PRIMORDIAL BROWN DWARF BINARY DISTRIBUTIONS FROM TURBULENT FRAGMENTATION

The formation of brown dwarfs (BDs) poses a key challenge to star formation theory. The observed dearth of nearby (⩽5 AU) BD companions to solar mass stars, known as the BD desert, as well as the tendency for low-mass binary systems to be more tightly bound than stellar binaries, has been cited as evidence for distinct formation mechanisms for BDs and stars. In this paper, we explore the implications of the minimal hypothesis that BDs in binary systems originate via the same fundamental fragmentation mechanism as stars, within isolated, turbulent giant molecular cloud cores. We demonstrate analytically that the scaling of specific angular momentum with turbulent core mass naturally gives rise to the BD desert, as well as wide BD binary systems. Further, we show that the turbulent core fragmentation model also naturally predicts that very low mass binary and BD/BD systems are more tightly bound than stellar systems. In addition, in order to capture the stochastic variation intrinsic to turbulence, we generate 104 model turbulent cores with synthetic turbulent velocity fields to show that the turbulent fragmentation model accommodates a small fraction of binary BDs with wide separations, similar to observations. Indeed, the picture which emerges from the turbulent fragmentation model is that a single fragmentation mechanism may largely shape both stellar and BD binary distributions during formation.
Constraint-Procedural Logic Generated Environments for Deep Q-learning Agent training and benchmarking

While training and benchmarking neural networks, a large and precise set of data and an efficient test environment are parts of a successful process. A good data set is usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper we focus on the specification of the properties of the solutions needed to build a data set rather than using common primitives of imperative programming, exploring the possibility to procedurally generate data-sets using constraint programming in Prolog. In this phase geometric predicates describe a virtual environment according to inter-space requirements. The second part is focused to test the generated data set in a machine learning context by means of an AI gym and space search techniques. We developed a deep Q-learning model based neural network agent in Python able to address the NP search problem in the virtual space; the agent has the goal to explore the generated virtual environment to seek for a target, improving its performance through a reinforced learning process.
A Unified Representation and Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays

We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation—in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. An augmentation of the corpus (Paragraph version) by including copies of major claims has further increased the performance.
Ramsey-Friedman optimality with banking time Ramsey-Friedman optimality with banking time

This paper conducts a Ramsey analysis within an endogenous growth cash-in-advance economy with policy commitment. Credit and money are alternative payment mechanisms that act as inputs into the household production of exchange. The credit is produced with a diminishing returns technology with Inada conditions that implies along the balanced-growth path a degree one homogeneity of effective banking time. This tightens the restrictions found within shopping time economies while providing a production basis for the Ramsey-Friedman optimum that suggests a special case of Diamond and Mirrlees (1971).
A Two-Stage Curriculum Training Framework for NMT

Neural Machine Translation (NMT) models 001 are typically trained on heterogeneous data 002 that are concatenated and randomly shufﬂed. 003 Curriculum training aims to present the data 004 to the NMT systems in a meaningful order. 005 In this work, we introduce a two-stage cur-006 riculum training framework for NMT where 007 we ﬁne-tune a base NMT model on subsets 008 of data, selected by both deterministic scoring 009 using pre-trained methods and online scoring 010 that consider prediction scores of the emerg-011 ing NMT model. Through extensive experi-012 ments on six language pairs comprising low-013 and high-resource languages from WMT’21, 014 we have shown that our curriculum strategies 015 consistently demonstrate better quality (up to 016 +2.2 BLEU improvement) and faster conver-017 gence (approximately 50% fewer updates)
Tensorised Probabilistic Inference for Neural Probabilistic Logic Programming

Neural Probabilistic Logic Programming (NPLP) languages have illustrated how to combine the neural paradigm with that of probabilistic logic programming. Together, they form a neural-symbolic framework integrating low-level perception with high-level reasoning. Such an integration has been shown to aid in the limited data regime and to facilitate better generalisation to out-of-distribution data. However, probabilistic logic inference does not allow for data-parallelisation because of the asymmetries arising in the proof trees during grounding. By lifting part of this inference procedure through the use of symbolic tensor operations, facilitating parallelisation, we achieve a measurable speed-up in learning and inference time. We implemented this tensor perspective in the NPLP language DeepProbLog and demonstrated the speed-up in a comparison to its regular implementation that utilises state-of-the-art probabilistic inference techniques.
HIGH-RESOLUTION OBSERVATIONS AND THE PHYSICS OF HIGH-VELOCITY CLOUD A0

The neutral hydrogen structure of high-velocity cloud A0 (at about −180 km s−1) has been mapped with a 9. ′1 resolution. Gaussian decomposition of the profiles is used to separately map families of components defined by similarities in center velocities and line widths. About 70% of the H i gas is in the form of a narrow, twisted filament whose typical line widths are of the order of 24 km s−1. Many bright features with narrow line widths of the order of 6 km s−1, clouds, are located in and near the filament. A third category with properties between those of the filament and clouds appears in the data. The clouds are not always co-located with the broader line width filament emission as seen projected on the sky. Under the assumption that magnetic fields underlie the presence of the filament, a theorem is developed for its stability in terms of a toroidal magnetic field generated by the flow of gas along field lines. It is suggested that the axial magnetic field strength may be derived from the excess line width of the H i emission over and above that due to kinetic temperature by invoking the role of Alfvén waves that create what is in essence a form of magnetic turbulence. At a distance of 200 pc the axial and the derived toroidal magnetic field strengths in the filament are then about 6 μG while for the clouds they are about 4 μG. The dependence of the derived field strength on distance is discussed.
A Formulation of a Matrix Sparsity Approach for the Quantum Ordered Search Algorithm

One specific subset of quantum algorithms is Grovers Ordered Search Problem (OSP), the quantum counterpart of the classical binary search algorithm, which utilizes oracle functions to produce a specified value within an ordered database. Classically, the optimal algorithm is known to have a $\log_2 N$ complexity; however, Grovers algorithm has been found to have an optimal complexity between the lower bound of $((\ln N-1)/\pi \approx 0.221\log_2 N)$ and the upper bound of $0 . 433\log_2 N$. We sought to lower the known upper bound of the OSP. With [E. Farhi et al, arXiv:quant-ph/9901059], we see that the OSP can be resolved into a translational invariant algorithm to create quantum query algorithm restraints. With these restraints, one can find Laurent polynomials for various $k$ -- queries -- and $N$ -- database sizes -- thus finding larger recursive sets to solve the OSP and effectively reducing the upper bound. These polynomials are found to be convex functions, allowing one to make use of convex optimization to find an improvement on the known bounds. According to [A. Childs et al, arXiv:quant-ph/0608161v1], semidefinite programming, a subset of convex optimization, can solve the particular problem represented by the constraints . We were able to implement a program abiding to their formulation of a semidefinite program (SDP), leading us to find that it takes an immense amount of storage and time to compute. To combat this setback, we then formulated an approach to improve results of the SDP using matrix sparsity. Through the development of this approach, along with an implementation of a rudimentary solver, we demonstrate how matrix sparsity reduces the amount of time and storage required to compute the SDP -- overall ensuring further improvements will likely be made to reach the theorized lower bound