Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem Cell Repor ts

Stem cell-based embryo models by cultured pluripotent and extra-embryonic lineage stem cells are novel platforms to model early post-implantation development. We showed that induced pluripotent stem cells (iPSCs) could form ITS (iPSCs and trophectoderm stem cells) and ITX (iPSCs, trophectoderm stem cells, and XEN cells) embryos, resembling the early gastrula embryo developed in vivo . To facilitate the efﬁcient and unbiased analysis of the stem cell-based embryo model, we set up a machine learning workﬂow to extract multi-dimensional features and perform quantiﬁcation of ITS embryos using 3D images collected from a high-content screening system. We found that different PSC lines differ in their ability to form embryo-like structures. Through high-content screening of small molecules and cytokines, we identiﬁed that BMP4 best promoted the morphogenesis of the ITS embryo. Our study established an innovative strategy to analyze stem cell-based embryo models and uncovered new roles of BMP4 in stem cell-based embryo models.
Phase II study of nivolumab in patients with genetic alterations in DNA damage repair and response who progressed after standard treatment for metastatic solid cancers (KM-06)

Background Immune-modulating antibodies targeting programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) have demonstrated promising antitumor efficacy in various types of cancers, especially highly mutated ones. Genetic alterations in DNA damage response and repair (DDR) genes can lead to genetic instability, often accompanied by a high tumor mutation burden (TMB). However, few studies have validated the aberration of DDR genes as a predictive biomarker for response to immune-modulating antibodies. Methods The KM-06 open-label, multicenter, single-arm, phase II trial evaluated the safety and efficacy of nivolumab in refractory solid cancers with DDR gene mutations assessed by clinically targeted sequencing. Nivolumab (3 mg/kg) was administered every 2 weeks until disease progression, unacceptable toxicity, or for 24 months. The primary endpoint was the objective response rate (ORR) as per RECIST V.1. 1 criteria. Results A total of 48 patients were enrolled in the study (median age 61, 58.3% male). The most common cancer type was colorectal cancer (41.7%), followed by prostate and biliary tract cancer (8.3% each). Eight patients achieved a partial response as their best overall response, resulting in an ORR of 17.8%. The disease control rate was 60.0%. The median progression-free survival was 2.9 months. Treatment-related adverse events of any grade and grade ≥3 occurred in 44 (91.7%) and 4 (8.3%) patients, respectively. Clinically targeted sequencing data inferred both TMB and microsatellite instability (MSI). Using a TMB cut-off of 12 mut/Mb, there were significant differences in overall survival (p=0.00035), progression-free survival (p=0.0061), and the best overall response (p=0.05). In the RNA sequencing analysis, nivolumab responders showed activation of the interleukin signaling pathway. Patients who experienced early progression presented high epithelial-mesenchymal transition signaling pathway activation. The responders exhibited a marked increase in PD-1−/Ki67+CD8 T cells at the early stage of treatment (C3D1) compared with non-responders (p=0.03). Conclusions In this phase II trial, nivolumab demonstrated moderate efficacy and manageable toxicity in patients with solid cancer harboring DDR gene mutations. A high TMB (>12 mut/Mb) and MSI score (>2.5) determined through clinically target sequencing presented significant discriminatory power for the nivolumab response. Trial registration number NCT04761744.
DetectiveReDASers at HSD-2Lang 2024: A New Pooling Strategy with Cross-lingual Augmentation and Ensembling for Hate Speech Detection in Low-resource Languages

This paper addresses hate speech detection in Turkish and Arabic tweets, contributing to the HSD-2Lang Shared Task. We propose a specialized pooling strategy within a soft-voting ensemble framework to improve classification in Turkish and Arabic language models. Our approach also includes expanding the training sets through cross-lingual translation, introducing a broader spectrum of hate speech examples. Our method attains F1-Macro scores of 0.6964 for Turkish (Subtask A) and 0.7123 for Arabic (Subtask B). While achieving these results, we also consider the computational overhead, striking a balance between the effectiveness of our unique pooling strategy, data augmentation, and soft-voting ensemble. This approach advances the practical application of language models in low-resource languages for hate speech detection.
Toward Sentiment Aware Semantic Change Analysis

This student paper explores the potential of augmenting computational models of semantic change with sentiment information. It tests the efficacy of this approach on the English SemEval of Lexical Semantic Change and its associated historical corpora. We first establish the feasibility of our approach by demonstrating that existing models extract reliable sentiment information from historical corpora, and then validate that words that underwent semantic change also show greater sentiment change in comparison to historically stable words. We then integrate sentiment information into standard models of semantic change for individual words, and test if this can improve the overall performance of the latter, showing mixed results. This research contributes to our understanding of language change by providing the first attempt to enrich standard models of semantic change with additional information. It taps into the multifaceted nature of language change, that should not be reduced only to binary or scalar report of change, but adds additional dimensions to this change, sentiment being only one of these. As such, this student paper suggests novel directions for future work in integrating additional, more nuanced information of change and interpretation for finer-grained semantic change analysis.
Allen Institute for AI @ SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages

In this paper, we describe Allen AI’s submission to the constrained track of the SIGTYP 2024 Shared Task. Using only the data provided by the organizers, we pretrained a transformer-based multilingual model, then finetuned it on the Universal Dependencies (UD) annotations of a given language for a downstream task. Our systems achieved decent performance on the test set, beating the baseline in most language-task pairs, yet struggles with subtoken tags in multiword expressions as seen in Coptic and Ancient Hebrew. On the validation set, we obtained ≥70% F1- score on most language-task pairs. In addition, we also explored the cross-lingual capability of our trained models. This paper highlights our pretraining and finetuning process, and our findings from our internal evaluations.
Threat Behavior Textual Search by Attention Graph Isomorphism

Cyber attacks cause over $1 trillion loss every year. An important task for cyber security analysts is attack forensics. It entails understanding malware behaviors and attack origins. However, existing automated or manual malware analysis can only disclose a subset of behaviors due to inherent difficulties (e.g., malware cloaking and obfuscation). As such, analysts often resort to text search techniques to identify existing malware reports based on the symptoms they observe, exploiting the fact that malware samples share a lot of similarity, especially those from the same origin. In this paper, we propose a novel malware behavior search technique that is based on graph isomorphism at the attention layers of Transformer models. We also compose a large dataset collected from various agencies to facilitate such research.Our technique outperforms state-of-the-art methods, such as those based on sentence embeddings and keywords by 6-14%. In the case study of 10 real-world malwares, our technique can correctly attribute 8 of them to their ground truth origins while using Google only works for 3 cases.
VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction

UNESCO has classified 2500 out of 7000 languages spoken worldwide as endangered. Attrition of a language leads to loss of traditional wisdom, folk literature, and the essence of the community that uses it. It is therefore imperative to bring digital inclusion to these languages and avoid its extinction. Low resource languages are at a greater risk of extinction. Lack of unsupervised Optical Character Recognition(OCR) methodologies for low resource languages is one of the reasons impeding their digital inclusion. We propose VOLTAGE - a contrastive learning based OCR methodology, leveraging auto-glyph feature recommendation for cluster-based labelling. We augment the labelled data for diversity and volume using image transformations and Generative Adversarial Networks. Voltage has been designed using Takri - a family of scripts used in 16th to 20th century in the Himalayan regions of India. We present results for Takri along with other Indic scripts (both low and high resource) to substantiate the universal behavior of the methodology. An accuracy of 95% for machine printed and 87% for handwritten samples on Takri script has been achieved. We conduct baseline and ablation studies along with building downstream use cases for Takri, demonstrating the usefulness of our work.
On the Intractability to Synthesize Factual Inconsistencies in Summarization

Factual consistency detection has gotten raised attention in the task of abstractive summarization. Many existing works rely on synthetic training data, which may not accurately reflect or match the inconsistencies produced by summarization models. In this paper, we first systematically analyze the shortcomings of the current methods in synthesizing inconsistent summaries. Current synthesis methods may fail to produce inconsistencies of coreference errors and discourse errors, per our quantitative and qualitative study. Then, employing the parameter-efficient finetuning (PEFT) technique, we discover that a competitive factual consistency detector can be achieved using thousands of real model-generated summaries with human annotations. Our study demonstrates the importance of real machine-generated texts with human annotation in NLG evaluation as our model outperforms the SOTA on the CoGenSumm, FactCC, Frank, and SummEval datasets.
Crop prediction and soil response to sugarcane straw removal

Crop prediction and soil response to sugarcane straw removal Concerns about global warming and climate change have triggered a growing demand for renewable energy. In this scenario, the interest in using sugarcane straw as raw material for energy production has increased. However, straw plays an important role in maintaining soil quality. In addition, uncertainties as to produced straw amount and the straw removal impact on the stalk yield have raised doubts as to the use this raw material. In this sense, the objective this study was evaluate the short-term (2-year) the sugarcane straw removal impacts on soil and yield modeling of sugarcane stalk and straw, using soil attributes of different layers. Two experiments were carried out in São Paulo state, Brazil: one at Capivari (sandy clay loam soil) and another at Valparaíso (sandy loam soil). We have tested five rates of straw removal (i.e., equivalent to 0, 25, 50, 75 and 100 %). Soil samples were taken from 0-2.5, 2.5-5, 5-10, 10-20 and 20-30 cm layers to analyze pH, total C and N, P, K, Ca, Mg, bulk density and soil penetration resistance. Plant samples were collected to determine the straw and stalk yield. The impacts caused by straw removal differed between the areas, however, they concentrated on the more soil superficial layer. In sandy clay loam soil, straw removal led to organic carbon depletion and soil compaction, while in the sandy loam soil the chemical attributes (i.e. Ca and Mg contents) were the most impacted. In general, the results suggest that straw removal causes reduction more significant in soil quality for the sandy clay loam soil. The results indicate the possibility to remove about half-straw amount deposited on soil’s surface (8.7 Mg ha straw remaining) without causing severe implications on the quality of this soil. In contrast, although any amount of straw was sufficient to cause alterations the quality of the sandy loam soil, these impacts were less intense and are not magnified with the increase of straw removal. It was possible to model sugarcane straw and stalk yield using soil attributes. The 0-20 cm layer was the most important layer in the stalk yield definition, whereas the 0-5 cm layer, which the impacts caused by the straw removal were concentrated, was less important. Thus, we noticed that impacts caused to soil by straw removal have little influence on crop productivity. Straw prediction has proved more complex and possibly requires additional information (e.g crop and climate information) for good results to be obtained. Overall, the results suggest that the planned removal of straw for energy purposes can occur in a sustainable way, but should take into account site conditions, e.g soil properties. However, long-term research with different approaches is still necessary, both to follow up and confirm our results, and to develop ways to reduce damage caused by this activity
Findings of the First Shared Task on Offensive Span Identification from Code-Mixed Kannada-English Comments

Effectively managing offensive content is crucial on social media platforms to encourage positive online interactions. However, addressing offensive contents in code-mixed Dravidian languages faces challenges, as current moderation methods focus on flagging entire comments rather than pinpointing specific offensive segments. This limitation stems from a lack of annotated data and accessible systems designed to identify offensive language sections. To address this, our shared task presents a dataset comprising Kannada-English code-mixed social comments, encompassing offensive comments. This paper outlines the dataset, the utilized algorithms, and the results obtained by systems participating in this shared task.
HAMiSoN-baselines at ClimateActivism 2024: A Study on the Use of External Data for Hate Speech and Stance Detection

The CASE@EACL2024 Shared Task addresses Climate Activism online through three subtasks that focus on hate speech detection (Subtask A), hate speech target classification (Subtask B), and stance detection (Subtask C) respectively.Our contribution examines the effect of fine-tuning on external data for each of these subtasks. For the two subtasks that focus on hate speech, we augment the training data with the OLID dataset, whereas for the stance subtask we harness the SemEval-2016 Stance dataset. We fine-tune RoBERTa and DeBERTa models for each of the subtasks, with and without external training data.For the hate speech detection and stance detection subtasks, our RoBERTa models came up third and first on the leaderboard, respectively. While the use of external data was not relevant on those tasks, we found that it greatly improved the performance on the hate speech target categorization.
KAUCUS - Knowledgeable User Simulators for Training Large Language Models

An effective multi-turn instruction-following assistant can be developed by creating a simulator that can generate useful interaction data. Apart from relying on its intrinsic weights, an ideal user simulator should also be able to bootstrap external knowledge rapidly in its raw form to simulate the multifarious diversity of text available over the internet. Previous user simulators generally lacked diversity, were mostly closed domain, and necessitated rigid schema making them inefficient to rapidly scale to incorporate external knowledge. In this regard, we introduce Kaucus, a Knowledge-Augmented User Simulator framework, to outline a process of creating diverse user simulators, that can seamlessly exploit external knowledge as well as benefit downstream assistant model training. Through two GPT-J based simulators viz., a Retrieval Augmented Simulator and a Summary Controlled Simulator we generate diverse simulator-assistant interactions. Through reward and preference model-based evaluations, we find that these interactions serve as useful training data and create more helpful downstream assistants. We also find that incorporating knowledge through retrieval augmentation or summary control helps create better assistants.
Emo-Gen BART - A Multitask Emotion-Informed Dialogue Generation Framework

This paper is the model description for the Emo-Gen BART dialogue generation architecture, as submitted to the SCI-CHAT 2024 Shared Task. The Emotion-Informed Dialogue Generation model is a multi-task BARTbased model which performs dimensional and categorical emotion detection and uses that information to augment the input to the generation models. Our implementation is trained and validated against the IEMOCAP dataset, and compared against contemporary architectures in both dialogue emotion classification and dialogue generation. We show that certain loss function ablations are competitive against the state-of-the-art single-task models.
MUCS@DravidianLangTech-2024: A Grid Search Approach to Explore Sentiment Analysis in Code-mixed Tamil and Tulu

Sentiment Analysis (SA) is a field of computational study that analyzes and understands people’s opinions, attitudes, and emotions toward any entity. A review of an entity can be written about an individual, an event, a topic, a product, etc., and such reviews are abundant on social media platforms. The increasing number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. In spite of this, SA of social media text is challenging because the code-mixed text is complex. To address SA in code-mixed Tamil and Tulu text, this paper describes the Machine Learning (ML) models submitted by our team - MUCS to “Sentiment Analysis in Tamil and Tulu - Dravidian- LangTech” - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024. Linear Support Vector classifier (LinearSVC) and ensemble of 5 ML classifiers (k Nearest Neighbour (kNN), Stochastic Gradient Descent (SGD), Logistic Regression (LR), LinearSVC, and Random Forest Classifier (RFC)) with hard voting trained using concatenated features obtained from word and character n-ngrams vectoized from Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and CountVectorizer. Further, Gridsearch algorithm is employed to obtain optimal hyperparameter values.The proposed ensemble model obtained macro F1 scores of 0.260 and 0.550 for Tamil and Tulu languages respectively.
Direct Speech Identification in Swedish Literature and an Exploration of Training Data Type, Typographical Markers, and Evaluation Granularity

Identifying direct speech in literary fiction is challenging for cases that do not mark speech segments with quotation marks. Such efforts have previously been based either on smaller manually annotated gold data or larger automatically annotated silver data, extracted from works with quotation marks. However, no direct comparison has so far been made between the performance of these two types of training data. In this work, we address this gap. We further explore the effect of different types of typographical speech marking and of using evaluation metrics of different granularity. We perform experiments on Swedish literary texts and find that using gold and silver data has different strengths, with gold data having stronger results on token-level metrics, whereas silver data overall has stronger results on span-level metrics. If the training data contains some data that matches the typographical speech marking of the target, that is generally sufficient for achieving good results, but it does not seem to hurt if the training data also contains other types of marking.
Representation and Generation of Machine Learning Test Functions

Writing tests for machine learning (ML) code is a crucial step towards ensuring the correctness and reliability of ML software. At the same time, Large Language Models (LLMs) have been adopted at a rapid pace for various code generation tasks, making it a natural choice for many developers who need to write ML tests. However, the implications of using these models, and how the LLM-generated tests differ from human-written ones, are relatively unexplored. In this work, we examine the use of LLMs to extract representations of ML source code and tests in order to understand the semantic relationships between human-written test functions and LLM-generated ones, and annotate a set of LLM-generated tests for several important qualities including usefulness, documentation, and correctness. We find that programmers prefer LLM-generated tests to those selected using retrieval-based methods, and in some cases, to those written by other humans.
Neuralign: A Context-Aware, Cross-Lingual and Fully-Neural Sentence Alignment System for Long Texts

Sentence alignment – establishing links between corresponding sentences in two related documents – is an important NLP task with several downstream applications, such as machine translation (MT). Despite the fact that existing sentence alignment systems have achieved promising results, their effectiveness is based on auxiliary information such as document metadata or machine-generated translations, as well as hyperparameter-sensitive techniques. Moreover, these systems often overlook the crucial role that context plays in the alignment process.In this paper, we address the aforementioned issues and propose Neuralign: the first context-aware, end-to-end and fully-neural architecture for sentence alignment. Our system maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. We extensively evaluate Neuralign on a multilingual dataset consisting of 20 language pairs derived from the Opus project, and demonstrate that our model achieves state-of-the-art performance. To ensure reproducibility, we release our code and model checkpoints at https://github.com/Babelscape/Neuralign.
ASR TAMIL SSN@ LT-EDI-2024: Automatic Speech Recognition system for Elderly People

The results of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil (LT-EDI-2024) are discussed in this paper. The goal is to create an automated system for Tamil voice recognition. The older population that speaks Tamil is the source of the dataset used in this task. The proposed ASR system is designed with pre-trained model akashsivanandan/wav2vec2-large-xls-r300m-tamil-colab-final. The Tamil common speech dataset is utilized to fine-tune the pretrained model that powers our system. The suggested system receives the test data that was released from the task; transcriptions are then created for the test samples and delivered to the task. Word Error Rate (WER) is the evaluation statistic used to assess the provided result based on the task. Our Proposed system attained a WER of 29.297%.
Transformers@LT-EDI-EACL2024: Caste and Migration Hate Speech Detection in Tamil Using Ensembling on Transformers

In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team “Transformers” submission to the Caste and Migration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste and Migration Hate Speech in Tamil shared task.