Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Bayesian computation for parameter inference and model selection in systems biology

Mathematical modelling has become an important tool in the biological sciences. Due to the overwhelming complexity of biological systems, it is not straightforward to determine the structure of the models. Moreover, the majority of parameter values are unknown and despite technological advances, these parameters are often difficult to measure experimentally. Therefore, statistical and computational techniques are needed to distinguish the good models from the unsuitable ones and to estimate unknown parameters. In this thesis we present a novel algorithm for parameter estimation and model selection of dynamical systems. The algorithm belongs to the class of approximate Bayesian computation (ABC) methods, which can evaluate posterior distributions without having to calculate Ukelihoods. It is based on a sequential Monte Carlo framework, which gives our method a computational advantage over other existing ABC methods. The algorithm is applied to a wide variety of biological systems such as prokaryotic and eukaryotic signalling and stress response pathways, gene regulatory networks, and infectious diseases. We illustrate its applicability to deterministic and stochastic models, and draw inferences from different data frameworks. Posterior parameter distributions are analysed in order to gain further insight into parameter sensitivity and sloppiness. The comprehensive analysis provided in this thesis illustrates the flexibihty of our new ABC SMC approach. The algorithm has proven useful for efficient parameter inference, systematic model selection and inference-based modelhng, and is a novel and useful addition to the systems biology toolbox.
PI Controllers for 1-D Nonlinear Transport Equation

In this paper, we introduce a method to obtain necessary and sufficient stability conditions for systems governed by one-dimensional nonlinear hyperbolic partial-differential equations with closed-loop integral controllers, when the linear frequency analysis cannot be used anymore. We study the stability of a general nonlinear transport equation where the control input and the measured output are both located on the boundaries. The principle of the method is to extract the limiting part of the stability from the solution using a projector on a finite-dimensional space and then use a Lyapunov approach. This paper improves a result of Trinh, Andrieu, and Xu, and gives an optimal condition for the design of the controller. The results are illustrated with numerical simulations where the predicted stable and unstable regions can be clearly identified.
Annotating and Training for Population Subjective Views

In this paper, we present a dataset of subjective views (beliefs and attitudes) held by individuals or groups. We analyze the usefulness of the dataset by training a neural classifier that identifies belief-containing sentences that are relevant for our broader project of interest—scientific modeling of complex systems. We also explore and discuss difficulties related to annotation of subjective views and propose ways of addressing them.
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap

Paraphrase detection is useful in many natural language understanding applications. Current works typically formulate this problem as a sentence pair binary classification task. However, this setup is not a good fit for many of the intended applications of paraphrase models. In particular, such applications often involve finding the closest paraphrases of the target sentence from a group of candidate sentences where they exhibit different degrees of semantic overlap with the target sentence. To apply models to this paraphrase retrieval scenario, the model must be sensitive to the degree to which two sentences are paraphrases of one another. However, many existing datasets ignore and fail to test models in this setup. In response, we propose adversarial paradigms to create evaluation datasets, which could examine the sensitivity to different degrees of semantic overlap. Empirical results show that, while paraphrase models and different sentence encoders appear successful on standard evaluations, measuring the degree of semantic overlap still remains a big challenge for them.
Corpus-Based Task-Specific Relation Discovery

Relation extraction is a crucial language processing task for various downstream applications, including knowledge base completion, question answering, and summarization. Traditional relation-extraction techniques, however, rely on a predefined set of relations and model the extraction as a classification task. Consequently, such closed-world extraction methods are insufficient for inducing novel relations from a corpus. Unsupervised techniques like OpenIE, which extract triples, generate relations that are too general for practical information extraction applications. In this work, we contribute the following: 1) We motivate and introduce a new task, corpus-based task-specific relation discovery. 2) We adapt existing data sources to create Wiki-Art, a novel dataset for task-specific relation discovery. 3) We develop a novel framework for relation discovery using zero-shot entity linking, prompting, and type-specific clustering. Our approach effectively connects unstructured text spans to their shared underlying relations, bridging the data-representation gap and significantly outperforming baselines on both quantitative and qualitative metrics. Our code and data are available in our GitHub repository.
On the Underspecification of Situations in Open-domain Conversational Datasets

Advances of open-domain conversational systems have been achieved through the creation of numerous conversation datasets. However, many of the commonly used datasets contain little or no information about the conversational situation, such as relevant objects/people, their properties, and relationships. This absence leads to underspecification of the problem space and typically results in undesired dialogue system behavior. This position paper discusses the current state of the field associated with processing situational information. An analysis of response generation using three datasets shows that explicitly provided situational information can improve the coherence and specificity of generated responses, but further experiments reveal that generation systems can be misled by irrelevant information. Our conclusions from this evaluation provide insights into the problem and directions for future research.
Can ChatGPT Understand Causal Language in Science Claims?

This study evaluated ChatGPT’s ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship. The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin. ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges. However, its weakness may be utilized to improve the clarity of human annotation guideline. Chain-of-Thoughts were faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens

Sequence-to-sequence paraphrase generation models often struggle with the generation of diverse paraphrases. This deficiency constrains the viability of leveraging paraphrase generation in different Natural Language Processing tasks. We propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. Our proposed method leverages multilingual neural machine translation pretraining to learn zero-shot paraphrasing. Furthermore, we incorporate dedicated prefix tokens into the training of the machine translation models to promote variation. The prefix tokens are designed to affect various linguistic features related to surface form realizations, and can be applied during inference to guide the decoding process towards a desired solution. We assess the proposed guided model on paraphrase generation in three languages, English, Finnish, and Swedish, and provide analysis on the feasibility of the prefix tokens to guided paraphrasing. Our analysis suggests that the attributes represented by the prefix tokens are useful in promoting variation, by pushing the paraphrases generated by the guided model to diverge from the input sentence while preserving semantics conveyed by the sentence well.
Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts

The automatic extraction of hypernym knowledge from large language models like BERT is an open problem, and it is unclear whether methods fail due to a lack of knowledge in the model or shortcomings of the extraction methods. In particular, methods fail on challenging cases which include rare or abstract concepts, and perform inconsistently under paraphrased prompts. In this study, we revisit the long line of work on pattern-based hypernym extraction, and use it as a diagnostic tool to thoroughly examine the hypernomy knowledge encoded in BERT and the limitations of hypernym extraction methods. We propose to construct prompts from established pattern structures: definitional (X is a Y); lexico-syntactic (Y such as X); and their anchored versions (Y such as X or Z). We devise an automatic method for anchor prediction, and compare different patterns in: (i) their effectiveness for hypernym retrieval from BERT across six English data sets; (ii) on challenge sets of rare and abstract concepts; and (iii) on consistency under paraphrasing. We show that anchoring is particularly useful for abstract concepts and in enhancing consistency across paraphrases, demonstrating how established methods in the field can inform prompt engineering.
Monolingual Phrase Alignment as Parse Forest Mapping

We tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. The proposed method achieves efficient alignment by mapping forests on a packed structure. The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.
BpHigh at WASSA 2023: Using Contrastive Learning to build Sentence Transformer models for Multi-Class Emotion Classification in Code-mixed Urdu

In this era of digital communication and social media, texting and chatting among individuals occur mainly through code-mixed or Romanized versions of the native language prevalent in the region. The presence of Romanized and code-mixed language develops the need to build NLP systems in these domains to leverage the digital content for various use cases. This paper describes our contribution to the subtask MCEC of the shared task WASSA 2023:Shared Task on Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages. We explore how one can build sentence transformers models for low-resource languages using unsupervised data by leveraging contrastive learning techniques described in the SIMCSE paper and using the sentence transformer developed to build classification models using the SetFit approach. Additionally, we’ll publish our code and models on GitHub and HuggingFace, two open-source hosting services.
Discourse Mode Categorization of Bengali Social Media Health Text

The scarcity of annotated data is a major impediment to natural language processing (NLP) research in Bengali, a language that is considered low-resource. In particular, the health and medical domains suffer from a severe paucity of annotated data. Thus, this study aims to introduce BanglaSocialHealth, an annotated social media health corpus that provides sentence-level annotations of four distinct types of expression modes, namely narrative (NAR), informative (INF), suggestive (SUG), and inquiring (INQ) modes in Bengali. We provide details regarding the annotation procedures and report various statistics, such as the median and mean length of words in different sentence modes. Additionally, we apply classical machine learning (CML) classifiers and transformer-based language models to classify sentence modes. We find that most of the statistical properties are similar in different types of sentence modes. To determine the sentence mode, the transformer-based M-BERT model provides slightly better efficacy than the CML classifiers. Our developed corpus and analysis represent a much-needed contribution to Bengali NLP research in medical and health domains and have the potential to facilitate a range of downstream tasks, including question-answering, misinformation detection, and information retrieval.
AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data

This paper presents a study on using the RoBERTa language model for emotion classification of essays as part of the ‘Shared Task on Empathy Detection, Emotion Classification and Personality Detection in Interactions’ organized as part of ‘WASSA 2023’ at ‘ACL 2023’. Emotion classification is a challenging task in natural language processing, and imbalanced datasets further exacerbate this challenge. In this study, we explore the use of various data balancing techniques in combination with RoBERTa to improve the classification performance. We evaluate the performance of our approach (denoted by adityapatkar on Codalab) on a benchmark multi-label dataset of essays annotated with eight emotion categories, provided by the Shared Task organizers. Our results show that the proposed approach achieves the best macro F1 score in the competition’s training and evaluation phase. Our study provides insights into the potential of RoBERTa for handling imbalanced data in emotion classification. The results can have implications for the natural language processing tasks related to emotion classification.
Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages

We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on code-mixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class. The participants were provided with a dataset of code-mixed SMS messages in English and Roman Urdu labeled with 12 emotions for both tracks. A total of 5 teams (19 team members) participated in the shared task. We summarized the methods, resources, and tools used by the participating teams. We also made the data freely available for further improvements to the task.
The Paradox of Multilingual Emotion Detection

The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.
LEXPLAIN: Improving Model Explanations via Lexicon Supervision

Model explanations that shed light on the model’s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model’s predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXplain, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the model’s explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) when performing the task, improving fairness.
User Simulator Assisted Open-ended Conversational Recommendation System

Conversational recommendation systems (CRS) have gained popularity in e-commerce as they can recommend items during user interactions. However, current open-ended CRS have limited recommendation performance due to their short-sighted training process, which only predicts one utterance at a time without considering its future impact. To address this, we propose a User Simulator (US) that communicates with the CRS using natural language based on given user preferences, enabling long-term reinforcement learning. We also introduce a framework that uses reinforcement learning (RL) with two novel rewards, i.e., recommendation and conversation rewards, to train the CRS. This approach considers the long-term goals and improves both the conversation and recommendation performance of the CRS. Our experiments show that our proposed framework improves the recall of recommendations by almost 100%. Moreover, human evaluation demonstrates the superiority of our framework in enhancing the informativeness of generated utterances.
Crack identification of functionally graded beam using distributed piezoelectric sensor

The present paper addresses development of a procedure for crack detection in functionally graded Timoshenko beam using a distributed piezoelectric sensor. Crack is represented by a pair of translational and rotational springs of stiffness calculated from its depth. A piezoelectric layer bonded to the beam is employed as a distributed sensor. Adopting the double beam model for the beam with the sensor, governing equations are conducted and utilized for establishing a database to propose a procedure for detecting a single crack in a functionally graded beam using the sensor output charge. The modal sensor charge provides a novel indicator more efficient for crack identification in functionally graded beams than natural frequencies. The effect of measurement noise, sensor thickness, and material parameters on crack identification has been investigated for beams with different boundary conditions.
The Impact of Electromagnetic Field on Conditioned Reflex Memory

The aim of this study is to investigate whether household frequency electromagnetic field affect memory processes. Apparently in various studies researchers note that different frequencies electromagnetic field improve or impair memory. In memory process hormone ghrelin plays an important role, which participates in the neurogenesis processes of memory formation. We have studied effect of electromagnetic field (EMF) on ghrelin`s concentration and memory changes under EMF. We exposed male Wistar rats under EMF. The household frequency electromagnetic field was generated with a GSM mobile phone. For memory tests we used the two feeder and five choice serial reaction time tests (5-CSRTT). With this latter test we studied attention and concentration and in serum we measured the concentration of ghrelin by immunoferminal analysis. In serum ghrelin concentration increased in electromagnetic field exposure group, after 30th day of the exposure level of ghrelin is higher than after 10 day of exposure and the sham control group. During training EMF exposure group rats task needed more trials (sessions) than in sham control group (in 5-CSRTT in sham control and EMF exposure groups, the values were 92,9±2,084 and 101,8±2,764, respectively. n=10; p＜0.05, in two feeder tests in sham control and EMF exposure groups, the values were 82,7±2,989 and 99,1±3,903, respectively. n=10; p＜0.05). As for memory consolidation, for performing 5-CSRTT after 30 day of EMF action we received significant differences in the number of correctly performed tasks (sham control and EMF exposure groups, the values were 0,7±0,1528 and 0,2±0,1333, respectively; p＜0.05) and results in prematurity activities (in sham control and EMF exposure groups 0,5 ± 0,2236 and 1,5 ± 0,3416, p＜0.05) and in accuracy (in sham control and EMF exposure groups 0,5 ± 0,1667and 1,4 ± 0,2667, p＜0.05). In our study It was found, that EMF affected as well as on learning and memory recall process, but with more advanced research reason, that clinically gave a picture of memory loss mainly related to decreased attention and concentration, which is also confirmed by the increase in concentration of hormone ghrelin, participating in memory processes, under influence of the household frequency EMF.
Models Having Tunable Parameters in Scale-free Networks

Based on typical scale-free network models (BA-model), we set up the partial differential equation satisfied more general dynamic networks. Starting from a more general situation, we establish two network models and show their topological properties that obey power laws. Keywords-scale-free models; dynamical evolution equation; small-world effect; social network
Multiplexing of spatial modes in the mid-IR region

Traditional optical communication systems optimize multiplexing in polarization and wavelength both trans- mitted in fiber and free-space to attain high bandwidth data communication. Yet despite these technologies, we are expected to reach a bandwidth ceiling in the near future. Communications using orbital angular momentum (OAM) carrying modes offers infinite dimensional states, providing means to increase link capacity by multiplexing spatially overlapping modes in both the azimuthal and radial degrees of freedom. OAM modes are multiplexed and de-multiplexed by the use of spatial light modulators (SLM). Implementation of complex amplitude modulation is employed on laser beams phase and amplitude to generate Laguerre-Gaussian (LG) modes. Modal decomposition is employed to detect these modes due to their orthogonality as they propagate in space. We demonstrate data transfer by sending images as a proof-of concept in a lab-based scheme. We demonstrate the creation and detection of OAM modes in the mid-IR region as a precursor to a mid-IR free-space communication link.
Chirality Driven by Magnetic Dipole Response for Demultiplexing of Surface Waves

Surface electromagnetic waves are characterized by the intrinsic spin‐orbit interaction which results in the fascinating spin‐momentum locking. Therefore, directional coupling of light to surface waves can be achieved through chiral nanoantennas. Here, we show that dielectric nanoantenna provides chiral response with strong spectral dependence due to the interference of electric and magnetic dipole momenta when placed in the vicinity of the metal‐air interface. Remarkably, chiral behaviour in the proposed scheme does not require elliptical polarization of the pump beam or the geometric chirality of the nanoantenna. We show that the proposed ultracompact and simple dielectric nanoantenna allows for both directional launching of surface plasmon polaritons on a thin gold film and their demultiplexing with a high spectral resolution.