Morphology in the Age of Pre-trained Language Models

The field of natural language processing (NLP) has adopted deep learning methods in the past 15 years. Nowadays the state-of-the-art in most NLP tasks is some kind of neural model, often the fine-tuned version of a pre-trained language model. The efficacy of these models is demonstrated on various English benchmarks and increasingly, other monolingual and multimultilingual benchmarks. In this dissertation I explore the application of deep learning models on low level tasks, particularly morphosyntactic tasks in multiple languages. The first part of this dissertation (Chapters 3 and 4) explores the application of deep learning models for classical morphosyntactic tasks such as morphological analysis and generation in dozens of languages with special focus on Hungarian. The second part of this dissertation (Chapters 5 to 8) deals with pre-trained language models, mostly models from the BERT family. I include some experiments on GPT-4o and GPT-4o-mini. These models show excellent performance on various tasks in English and some high density languages. However, their evaluation in medium and low density languages is lacking. I present a methodology for generating morphosyntactic benchmarks in arbitrary languages and I analyze multiple BERT-like models in detail. My main tool for analysis is the probing methodology which I extend the with perturbations, the systematic removal of certain information from the sentence. I use Shapley values to further refine my analysis

A Machine-Learning-Based Analysis of Resting Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students

Background: Early and accurate diagnosis is crucial for effective prevention and treatment of severe mental illnesses, such as schizophrenia and bipolar disorder. However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable of detecting latent disease liability in healthy volunteers. Methods: Using questionnaires examining affective temperament and schizotypal traits among voluntary, healthy university students (N = 710), we created three groups. These were a group characterized by an emphasis on positive schizotypal traits (N = 20), a group showing cyclothymic temperament traits (N = 17), and a control group showing no susceptibility in either direction (N = 21). We performed a resting-state EEG examination as part of a complex psychological, electrophysiological, psychophysiological, and laboratory battery, and we developed feature-selection machine-learning methods to differentiate the low-risk groups. Results: Both low-risk groups could be reliably (with 90% accuracy) separated from the control group. Conclusions: Models applied to the data allowed us to differentiate between healthy university students with latent schizotypal or bipolar tendencies. Our research may improve the sensitivity and specificity of risk-state identification, leading to more effective and safer secondary prevention strategies for individuals in the prodromal phases of these disorders

Diffusive network connected chemical reaction networks

A novel type of networked Chemical Reaction Network (CRN) model is proposed in this paper where the CRN subsystem nodes are connected in a bi-directional state-difference-driven way, forming a diffusive network. The diffusive connecting elements can also be dynamic and can be modelled as open linear CRN subsystems. It has been shown that the overall model of the diffusively connected CRN network is also in a CRN form with block-structured complex composition and Kirchhoff matrices that can be constructed from the network topology and the individual models of the network nodes. Considering static diffusive manipulable and disturbance inputs, setpoint tracking and disturbance attenuation controllers have also been proposed. The properties of the introduced model and controller are illustrated through a case study

Migration-connected networks of Lotka–Volterra and quasi-polynomial systems: modeling and decentralized control

This paper introduces a modeling and a control approach for Lotka-Volterra systems that are interconnected through population size-dependent migration flows. First, a control-oriented model is proposed for networks of Lotka-Volterra systems. Based on this model, a decentralized control method is introduced which assures that the states of each Lotka-Volterra system in the network can be driven into a prescribed setpoint regardless of migration. The results have been generalized to quasi-polynomial systems, and networks of Lotka-Volterra systems having interconnections with distributed delay. Simulation experiments are also presented in the paper to show the implementability of the theoretical results

Tournament design: A review from an operational research perspective

Every sport needs rules. Tournament design refers to the rules that determine how a tournament, a series of games between a number of competitors, is organized. This study aims to provide an overview of the tournament design literature from the perspective of operational research. Three important design criteria are discussed: efficacy and effectivity, fairness, and attractiveness. Our survey classifies the papers discussing these properties according to the main components of tournament design: format, seeding, draw, scheduling, and ranking. We also outline several open questions and promising directions for future research

Efficient Moving Object Segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps

LiDAR point clouds are a rich source of information for autonomous vehicles and ADAS systems. However, they can be challenging to segment for moving objects as - among other things - finding correspondences between sparse point clouds of consecutive frames is difficult. Traditional methods rely on a (global or local) map of the environment, which can be demanding to acquire and maintain in real-world conditions and the presence of the moving objects themselves. This paper proposes a novel approach using as minimal sweeps as possible to decrease the computational burden and achieve mapless moving object segmentation (MOS) in LiDAR point clouds. Our approach is based on a multimodal learning model with single-modal inference. The model is trained on a dataset of LiDAR point clouds and related camera images. The model learns to associate features from the two modalities, allowing it to predict dynamic objects even in the absence of a map and the camera modality. We propose semantic information usage for multi-frame instance segmentation in order to enhance performance measures. We evaluate our approach to the SemanticKITTI and Apollo real-world autonomous driving datasets. Our results show that our approach can achieve state-of-the-art performance on moving object segmentation and utilize only a few (even one) LiDAR frames. The implementation with examples and pre-trained networks is available: https://github.com/madak88/2DPASS-MOS © 2020 IEEE