pwc-archive/papers-with-abstracts huggingface dataset:
Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling
Authors: Viktoria Heimann, Andreas Spruck, André Kaup
Date: 2022-10-19 00:00:00

High frame rates are desired in many fields of application. As in many cases the frame repetition rate of an already captured video has to be increased, frame rate up-conversion (FRUC) is of high interest. We conduct a motion compensated approach. From two neighboring frames, the motion is estimated and the neighboring pixels are shifted along the motion vector into the frame to be reconstructed. For displaying, these irregularly distributed mesh pixels have to be resampled onto regularly spaced grid positions. We use the model-based key point agnostic frequency-selective mesh-to-grid resampling (AFSMR) for this task and show that AFSMR works best for applications that contain irregular meshes with varying densities. AFSMR gains up to 3.2 dB in contrast to the already high performing frequency-selective mesh-to-grid resampling (FSMR). Additionally, AFSMR increases the run time by a factor of 11 relative to FSMR.
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks
Authors: Luca Barbieri, Osvaldo Simeone, Monica Nicoli
Date: 2022-10-19 00:00:00
Tasks: Decision Making, Federated Learning

Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space. This paper focuses on Bayesian FL implemented in a device-to-device (D2D) network via Decentralized Stochastic Gradient Langevin Dynamics (DSGLD), a recently introduced gradient-based Markov Chain Monte Carlo (MCMC) method. Based on the observation that DSGLD applies random Gaussian perturbations of model parameters, we propose to leverage channel noise on the D2D links as a mechanism for MCMC sampling. The proposed approach is compared against a conventional implementation of frequentist FL based on compression and digital transmission, highlighting advantages and limitations.
Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface
Authors: Reza Ghazalian, Hui Chen, George C. Alexandropoulos, Gonzalo Seco-Granados, Henk Wymeersch, Riku Jäntti
Date: 2022-10-18 00:00:00
Tasks: parameter estimation, Position

The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing. However, to accurately localize a user placed in the area of influence of an RIS, the RIS location needs to be known a priori and its phase profile is required to be optimized for localization. In this paper, we study the problem of the joint localization of a hybrid RIS (HRIS) and a user, considering that the former is equipped with a single reception radio-frequency (RF) chain enabling simultaneous tunable reflections and sensing via power splitting. Focusing on the downlink of a multi-antenna base station, we present a multi-stage approach for the estimation of the HRIS position and orientation as well as the user position. Our simulation results, including comparisons with the Cram\'er-Rao lower bounds, demonstrate the efficiency of the proposed localization approach, while showcasing that there exists an optimal HRIS power splitting ratio for the desired multi-parameter estimation problem.
General non-linear imitation leads to limit cycles in eco-evolutionary dynamics
Authors: YuAn Liu, Lixuan Cao, Bin Wu
Date: 2022-10-19 00:00:00

Eco-evolutionary dynamics is crucial to understand how individuals' behaviors and the surrounding environment interplay with each other. Typically, it is assumed that individuals update their behaviors via linear imitation function, i.e., the replicator dynamics. It has been proved that there cannot be limit circles in such eco-evolutionary dynamics. It suggests that eco-evolutionary dynamics alone is not sufficient to explain the widely observed fluctuating behavior in both the biological and social systems. We extrapolate from the linear imitation function to general imitation function, which can be non-linear. It is shown that the general imitation does not change the internal equilibrium and its local stability. It however leads to limit cycles, which are never present in classical eco-evolutionary dynamics. Moreover, the average cooperation level and average environment state agree with the first and the second component of the internal fixed point. Furthermore, we estimate the location of the emergent limit cycle. On the one hand, our results provide an alternative mechanism to make cooperators and defectors coexist in a periodic way in the "tragedy of the commons" and indicates the global eco-evolutionary dynamics is sensitive to the imitation function. Our work indicates that the way of imitation, i.e., the imitation function, is crucial in eco-evolutionary dynamics, which is not true for evolutionary dynamics with a static environment.
The effect of "fear" on two species competition
Authors: Vaibhava Srivastava, Eric M. Takyi, Rana D. Parshad
Date: 2022-10-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Non-consumptive effects such as fear of depredation, can strongly influence predator-prey dynamics. These effects have not been as well studied in the case of purely competitive systems, despite ecological and social motivations for the same. In this work we consider the classic two species ODE and PDE Lokta-Volterra competition models, where \emph{one} of the competitors is "fearful" of the other. We find that the presence of fear can have several interesting dynamical effects on the classical scenarios of weak and strong competition, and competitive exclusion. Notably, for fear levels in certain regimes, we show bi-stability between interior equilibrium and boundary equilibrium is possible - contrary to the classical strong competition situation where bi-stability is only possible between boundary equilibrium. Furthermore, in the spatially explicit setting, the effects of several spatially heterogeneous fear functions are investigated. In particular, we show that under certain $\mathbb{L}^{1}$ restrictions on the fear function, a weak competition type situation can change to competitive exclusion. Applications of these results to ecological as well as sociopolitical settings are discussed, that connect to the "landscape of fear" (LOF) concept in ecology.
Expectations Formation with Fat-tailed Processes: Evidence from Sales Forecasts
Authors: Eugene Larsen-Hallock, Adam Rej, David Thesmar
Date: 2022-10-18 00:00:00

We empirically analyze a large sample of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns.
Optimal investment and reinsurance under exponential forward preferences
Authors: Katia Colaneri, Alessandra Cretarola, Benedetta Salterini
Date: 2022-10-19 00:00:00

We study the optimal investment and proportional reinsurance problem of an insurance company, whose investment preferences are described via a forward dynamic utility of exponential type in a stochastic factor model allowing for a possible dependence between the financial and insurance markets. Specifically, we assume that the asset price process dynamics and the claim arrival intensity are both affected by a common stochastic process and we account for a possible environmental contagion effect through the non-zero correlation parameter between the underlying Brownian motions driving the asset price process and the stochastic factor dynamics. By stochastic control techniques, we construct a forward dynamic exponential utility, and we characterize the optimal investment and reinsurance strategy. Moreover, we investigate in detail the zero-volatility case and provide a comparison analysis with classical results in an analogous setting under backward utility preferences. We also discuss an extension of the conditional certainty equivalent. Finally, we perform a numerical analysis to highlight some features of the optimal strategy.
POGD: Gradient Descent with New Stochastic Rules
Authors: Feihu Han, Sida Xing, Sui Yang Khoo
Date: 2022-10-15 00:00:00
Tasks: image-classification, Image Classification

There introduce Particle Optimized Gradient Descent (POGD), an algorithm based on the gradient descent but integrates the particle swarm optimization (PSO) principle to achieve the iteration. From the experiments, this algorithm has adaptive learning ability. The experiments in this paper mainly focus on the training speed to reach the target value and the ability to prevent the local minimum. The experiments in this paper are achieved by the convolutional neural network (CNN) image classification on the MNIST and cifar-10 datasets.
OntoPopulis, a System for Learning Semantic Classes
Authors: Hristo Tanev
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

Ontopopulis is a multilingual weakly supervised terminology learning algorithm which takes on its input a set of seed terms for a semantic category and an unannotated text corpus. The algorithm learns additional terms, which belong to this category. For example, for the category “environmental disasters” the input seed set in English is environmental disaster, water pollution, climate change. Among the highest ranked new terms which the system learns for this semantic class are deforestation, global warming and so on.
A corpus for Automatic Article Analysis
Authors: Elena Callegari, Desara Xhura
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Articles, Text Generation

We describe the structure and creation of the SageWrite corpus. This is a manually annotated corpus created to support automatic language generation and automatic quality assessment of academic articles. The corpus currently contains annotations for 100 excerpts taken from various scientific articles. For each of these excerpts, the corpus contains (i) a draft version of the excerpt (ii) annotations that reflect the stylistic and linguistics merits of the excerpt, such as whether or not the text is clearly structured. The SageWrite corpus is the first corpus for the fine-tuning of text-generation algorithms that specifically addresses academic writing.
Razmecheno: Named Entity Recognition from Digital Archive of Diaries “Prozhito”
Authors: Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, Ekaterina Artemova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

The vast majority of existing datasets for Named Entity Recognition (NER) are built primarily on news, research papers and Wikipedia with a few exceptions, created from historical and literary texts. What is more, English is the main source for data for further labelling. This paper aims to fill in multiple gaps by creating a novel dataset “Razmecheno”, gathered from the diary texts of the project “Prozhito” in Russian. Our dataset is of interest for multiple research lines: literary studies of diary texts, transfer learning from other domains, low-resource or cross-lingual named entity recognition. Razmecheno comprises 1331 sentences and 14119 tokens, sampled from diaries, written during the Perestroika. The annotation schema consists of five commonly used entity tags: person, characteristics, location, organisation, and facility. The labelling is carried out on the crowdsourcing platfrom Yandex.Toloka in two stages. First, workers selected sentences, which contain an entity of particular type. Second, they marked up entity spans. As a result 1113 entities were obtained. Empirical evaluation of Razmecheno is carried out with off-the-shelf NER tools and by fine-tuning pre-trained contextualized encoders. We release the annotated dataset for open access.
Filtering of Noisy Web-Crawled Parallel Corpus: the Japanese-Bulgarian Language Pair
Authors: Iglika Nikolova-Stoupak, Shuichiro Shimizu, Chenhui Chu, Sadao Kurohashi
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Machine Translation, Translation

One of the main challenges within the rapidly developing field of neural machine translation is its application to low-resource languages. Recent attempts to provide large parallel corpora in rare language pairs include the generation of web-crawled corpora, which may be vast but are, unfortunately, excessively noisy. The corpus utilised to train machine translation models in the study is CCMatrix, provided by OPUS. Firstly, the corpus is cleaned based on a number of heuristic rules. Then, parts of it are selected in three discrete ways: at random, based on the “margin distance” metric that is native to the CCMatrix dataset, and based on scores derived through the application of a state-of-the-art classifier model (Acarcicek et al., 2020) utilised in a thematic WMT shared task. The performance of the issuing models is evaluated and compared. The classifier-based model does not reach high performance as compared with its margin-based counterpart, opening a discussion of ways for further improvement. Still, BLEU scores surpass those of Acarcicek et al.’s (2020) paper by over 15 points.
Hate Speech Classification in Bulgarian
Authors: Radoslav Ralev, Jürgen Pfeffer
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Classification

In recent years, we have seen a surge in the propagation of online hate speech on social media platforms. According to a multitude of sources such as the European Council, hate speech can lead to acts of violence and conflict on a broader scale. That has led to in- creased awareness by governments, companies, and the scientific community, and although the field is relatively new, there have been considerable advancements in the field as a result of the collective effort. Despite the increasingly better results, most of the research focuses on the more popular languages (i.e., English, German, or Arabic), whereas less popular languages such as Bulgarian and other Balkan languages have been neglected. We have aggregated a real-world dataset from Bulgarian online forums and manually annotated 108,142 sentences. About 1.74% of which can be described with the categories racism, sexism, rudeness, and profanity. We then developed and evaluated various classifiers on the dataset and found that a support vector machine with a linear kernel trained on character-level TF-IDF features is the best model. Our work can be seen as another piece in the puzzle to building a strong foundation for future work on hate speech classification in Bulgarian.
WordNet-Based Bulgarian Sign Language Dictionary of Crisis Management Terminology
Authors: Slavina Lozanova, Ivelina Stoyanova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Management

This paper presents an online Bulgarian sign language dictionary covering terminology related to crisis management. The pressing need for such a resource became evident during the COVID pandemic when critical information regarding government measures was delivered on a regular basis to the public including Deaf citizens. The dictionary is freely available on the internet and is aimed at the Deaf, sign language interpreters, learners of sign language, social workers and the wide public. Each dictionary entry is supplied with synonyms in spoken Bulgarian, a definition, one or more signs corresponding to the concept in Bulgarian sign language, additional information about derivationally related words and similar signs with different meaning, as well as links to translations in other languages, including American sign language.
Raising and Control Constructions in a Bulgarian UD Parsebank of Parliament Sessions
Authors: Petya Osenova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

The paper discusses the raising and control syntactic structures (marked as ‘xcomp’) in a UD parsed corpus of Bulgarian Parliamentary Sessions. The idea is: to investigate the linguistic status of this phenomenon in an automatically parsed corpus, with a focus on verbal constructions of a head and its dependant together with the shared subject; to detect the errors and get insights on how to improve the annotation scheme and the automatic detection of this phenomenon realizations in Bulgarian.
Syntactic characteristics of emotive predicates in Bulgarian: A corpus-based study
Authors: Yovka Tisheva, Marina Dzhonova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

The paper presents a corpus-based study of emotive predicates (verbs and predicative constructions with adjectival, adverbial or noun phrases) in Bulgarian with respect to their syntactic characteristics. The sources of empirical data analyzed here are Bulgarian National Corpus, Corpus of Bulgarian Political and Journalistic Speech and Bulgarian part of Multilingual Comparable Corpora of Parliamentary Debates ParlaMint. The analyzes are organized in terms of morpho-syntactic features of emotive predicates, transitivity, syntactic functions and theta-roles of their arguments. Emotive predicates denote a state or an event involving an affective experience. As part of the special semantic class of psychological/Experiencer verbs, they have been studied in relation to the interaction between lexical semantics and argument realization. Bulgarian data confirm the well-established division of Psych predicates into three classes: Subject Experiencer (fear type verbs), Object Experiencer (frighten type verbs), Dative Experiencer. The third class is mostly represented by adverbial predicates.
Evidential strategies and grammatical marking in clauses governed by verba dicendi in Bulgarian
Authors: Ekaterina Tarpomanova, Krasimira Aleksova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

Thе study explores the interaction between the participants in the communication process with respect to their knowledge about the situation presented in the utterance when transforming direct into indirect speech using a verbum dicendi. The speaker has a choice between firsthand (indicative tenses) which by definition denotes a witnessed situation and non-firsthand which presents the situation as non-witnessed. The interplay between the grammatical marking and the speaker’s evidential strategy is analyzed by applying a corpus method. The data of the Bulgarian National Corpus are used to detect the preferences for a given strategy considering also the grammatical person which indicates the level of knowledge of the communicants about the situation: the 1st person shows the strong knowledge of the speaker, the 2nd person is related to the strong knowledge of the listener, and the 3rd person is associated with a weak knowledge of both participants. Illustrative examples representative for a given situation are extracted from the corpus and subjected to a context analysis.
Corpus-Based Research into Verb-Forming Suffixes in English: Its Empirical and Theoretical Consequences
Authors: Junya Morita
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

The present study explores the semantic and structural aspects of word formation processes in English, focusing on how verbs are derived by the suffixes -ize, -ify, -en, and -ate. Based on relevant derivatives extracted from the British National Corpus, their detailed observation is made from semantic and formal viewpoints. Then their theoretical analysis is carried out in the framework of generative theory. The BNC survey demonstrates that (i) the meanings of derived verbs are largely divided into five types and the submeanings are closely related to each other, (ii) the well-formedness of derived verbs is primarily determined by the semantic and formal features of their bases, and (iii) -ize suffixation is creative enough to provide a constant supply for new labels. To account for these empirical observations, the mechanism for forming -ize derivatives is proposed in which the semantic properties and creativity of -ize derivation stem solely from the underlying structure and the formal properties of the bases derive from the lexical entry of -ize.
Some Notes on p(e)re-Reduplication in Bulgarian and Ukrainian: A Corpus-based Study
Authors: Ivan Derzhanski, Olena Siruk
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

We present a comparative study of p(e)re-reduplication in Bulgarian and Ukrainian, based on material from a parallel corpus of bilingual texts. We analyse all occurrences found in the corpus of close sequences and conjunctions of two cognate words, the second of which features the intensive and recursive prefix pre- (Bulgarian) or pere- (Ukrainian). We find that in Bulgarian this construction occurs more frequently with finite verb forms, and in Ukrainian with participles and nouns. There is also a correlation with the mode of action denoted by the prefix: in its intensive meaning it turns up more often in Bulgarian, in its recursive meaning in the two languages equally, and in Ukrainian there are more occasions where it cannot be identified as either intensive or recursive. Finally, in both languages instances of p(e)re-reduplication are most common, by a wide marge, in texts with Ukrainian originals.
Zero-shot Event Causality Identification with Question Answering
Authors: Daria Liakhovets, Sven Schlarb
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Articles, Event Causality Identification, Multiple-choice, Passage Retrieval, Question Answering, Reading Comprehension, Relation, Relation Classification, Relation Extraction, Retrieval, Semantic Similarity, Semantic Textual Similarity

Extraction of event causality and especially implicit causality from text data is a challenging task. Causality is often treated as a specific relation type and can be considered as a part of relation extraction or relation classification task. Many causality identification-related tasks are designed to select the most plausible alternative of a set of possible causes and consider multiple-choice classification settings. Since there are powerful Question Answering (QA) systems pretrained on large text corpora, we investigated a zero-shot QA-based approach for event causality extraction using a Wikipedia-based dataset containing event descriptions (articles) and annotated causes. We aimed to evaluate to what extent reading comprehension ability of the QA-pipeline can be used for event-related causality extraction from plain text without any additional training. Some evaluation challenges and limitations of the data were discussed. We compared the performance of a two-step pipeline consisting of passage retrieval and extractive QA with QA-only pipeline on event-associated articles and mixed ones. Our systems achieved average cosine semantic similarity scores of 44 – 45% in different settings.