Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaching Maximum Likelihood Decoding Performance via Reshuffling ORBGRAND

Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance gap in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.
A Convex Hull Cheapest Insertion Heuristic for the Non-Euclidean TSP

The convex hull cheapest insertion heuristic is known to generate good solutions to the Traveling Salesperson Problem in Euclidean spaces, but it has not been extended to the non-Euclidean case. To address the difficulty of dealing with obstacles in the non-Euclidean space, the proposed adaptation uses multidimensional scaling to first approximate these points in a Euclidean space, thereby enabling the generation of the convex hull that initializes the algorithm. To evaluate the proposed algorithm, the TSPLIB benchmark data-set is modified by adding impassable separators that produce non-Euclidean spaces. The algorithm is demonstrated to outperform the commonly used Nearest Neighbor algorithm in 96% of the cases studied.
One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering

In this paper, we propose a novel multi-view clustering model, named Dual-space Co-training Large-scale Multi-view Clustering (DSCMC). The main objective of our approach is to enhance the clustering performance by leveraging co-training in two distinct spaces. In the original space, we learn a projection matrix to obtain latent consistent anchor graphs from different views. This process involves capturing the inherent relationships and structures between data points within each view. Concurrently, we employ a feature transformation matrix to map samples from various views to a shared latent space. This transformation facilitates the alignment of information from multiple views, enabling a comprehensive understanding of the underlying data distribution. We jointly optimize the construction of the latent consistent anchor graph and the feature transformation to generate a discriminative anchor graph. This anchor graph effectively captures the essential characteristics of the multi-view data and serves as a reliable basis for subsequent clustering analysis. Moreover, the element-wise method is proposed to avoid the impact of diverse information between different views. Our algorithm has an approximate linear computational complexity, which guarantees its successful application on large-scale datasets. Through experimental validation, we demonstrate that our method significantly reduces computational complexity while yielding superior clustering performance compared to existing approaches.
Topically diversified summarization of customer reviews

Promoting information coverage and sentence diversity is an efficient method to handle the fundamental issue of data heterogeneity or redundancy in multi-document summarization. We introduce a self-supervised algorithm for multi-document summarization that employs a multitask learning approach for topic diversification. Our model is based on two variational autoencoders that combine the training of a language model and a topic model to bias text generation and control the topic content of the produced summaries. We evaluate our method on the Amazon product review dataset and report ROUGE results and other metrics to assess information coverage. We demonstrate that our approach creates diversified outputs for the same batch of reviews and aspect-focused ones, allowing us to optimize text generation strategies.
DAP-LeR-DAug: Techniques for enhanced Online Sexism Detection

The swift surge of digital communication on social media platforms has brought about an increase in hate speech online, especially sex-ism. Such content can have devastating effects on the psychological well-being of the users, and it becomes imperative to design automated systems that can identify and flag such harmful content. Human moderation alone is inadequate to manage the volume of content, necessitating efficient technological solutions. In this study, we explore the performance of different modern techniques on Bert-based models for detecting sexist text. We explore four such techniques, namely, Domain Adaptive Pre-training (DAP), Learning Rate Scheduling (LeR), Data Augmentation (DAug), and an ensemble of all three. The results show that each technique improves performance differently on each task due to their different approaches, which may be suited to a certain problem more. The ensemble model performs the best in all three subtasks. These models are trained on a Se-meval’23 shared task dataset, which includes both sexist and non-sexist texts. All in all, this study explores the potential of DAP-LeR-DAug techniques in detecting sexist content. The re-sults of this study highlight the strengths and weaknesses of the three different techniques with respect to each subtask. The results of this study will be useful for researchers and developers interested in developing systems for identifying and flagging online hate speech.
KMD: A New Kurdish Multilabel Emotional Dataset For the Kurdish Sorani Dialect

This paper presents a new Kurdish Multilabel Emotional Dataset (KMD) for the Kurdish So-rani dialect, which contains emotional labels for four different categories: Fear, Sadness, Joy, and Surprise. The dataset was collected using Twitter API, pre-processed, and manually labelled by three independent annotators. We also conducted experiments using classical machine learning classifiers, including Naive Bayes and Support Vector Machine (SVM), and deep learning models, BLSTM and BERT, to evaluate the efficiency of the dataset. Our re-sults show that the multilingual BERT model outperforms the traditional machine learning classifiers in emotional labelling accuracy. The KMD dataset can be used for various natural language processing tasks, including sentiment analysis, emotion detection, and opinion mining, for the Kurdish Sorani dialect.
Comparing Modular and End-To-End Approaches in ASR for Well-Resourced and Low-Resourced Languages

We present a comparative study of a state-of-the-art traditional modular Automatic Speech Recognition (Kaldi ASR) and an end-to-end ASR (wav2vec 2.0) for a well-resourced language (Spanish) and a low-resourced language (Irish). We created ASRs for both languages and evaluated their performance under different update regimes. Our results show that the end-to-end wav2vec 2.0 outperforms the modular ASR for both languages in terms of Word Error Rate (WER) but performs worst in terms of real-time decoding. We also addressed the issue of non-lexical words in wav2vec 2.0’s output. We found that in wav2vec 2.0 by LM integration with shallow fusion and increasing LM weight to 0.7 and 0.8 respectively for the Spanish and Irish provided the optimum ASR performance by reducing non-lexical words. However, this does not eliminate all non-lexical words. Finally, our study found that Kaldi ASR would perform best for real-time decoding for longer audio inputs compared to wav2vec 2.0 model trained on the same dataset on the minimal infrastructure, although wav2vec 2.0’s performance can be improved with a GPU acceleration in backend. These results may have significant implications for creating real-time ASR services, especially for low-resourced languages.
iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Languages

Benchmark datasets are crucial for evaluating algorithms and models objectively. They provide a standardized basis for comparisons, promote reproducibility, and drive innovation by establishing baselines and encouraging advancements in the field. Limited benchmark datasets exist for various natural language processing tasks in low-resource languages, including most Philippine languages. As part of iTANONG’s 10 billion token dataset initiative, the authors release the first iteration of iTANONG-DS 1 , a collection of unlabeled and labeled datasets for different NLP tasks such as sentiment analysis, part-of-speech tagging, named entity recognition for Tagalog, and language modeling for Cebuano.
Enrichment of Arabic WordNet Using Machine Translation and Transformers

This article aims to enhance Arabic WordNet (AWN) by exploiting the current version of the Princeton WordNet (PWN) and Deep Learning (DL) techniques, known for their effectiveness in machine translation. We aim to improve the coverage and quality of AWN by adding new Synsets, integrating definitions and examples, and validating semantic relationships. The contribution can be summarized in three aspects: (1) utilizing multiple translation systems to translate PWN resources and web-extracted data into Arabic; (2) employing Transformers, a highly effective deep learning technique, to refine the outcomes from the first step; and (3) developing a web portal that enables users to visualize proposed updates and facilitates validation by human experts. The results from the evaluation of a random sample of 1,000 Synsets taken from the candidate pool for enrichment are highly promising, with a manual validation accuracy of 75.4%. With such a validation accuracy, the potential would be to get almost 68,000 Synsets correct out of the 90,127 candidates produced by our approach.
The interplay between moult of flight feathers and fuelling conducted on the breeding grounds of the Great Snipe Gallinago media from the eastern European, lowland population

The Great Snipe as a long-distant migrant wintering in Africa, faces the challenge of accumulating sufficient energy reserves before the departure from European breeding grounds. Despite possible trade-offs in resource allocation, this species additionally initiates moult of flight feathers before southward migration. Here we discuss the strategy of flight feather moult and fuelling, exploring their scheduling, constrained by the timing of breeding and departure for female and male Great Snipes from the European lowland population. We found significant intrasexual differences in both moult initiation date and moult duration. Males start flight feather replacement more than two weeks earlier and moult faster compared to females. However, neither sex completed this process on breeding grounds before the migration, as late in the season all males and half of the females had suspended their primary moult, with the remaining females not moulting at all. Moult of secondaries occurred exceptionally in the studied population. We observed a non-linear energetic stores gain in the studied period, where both sexes maintained a stable and low body condition until the end of July, coinciding with the primary moulting period. Subsequently, there was an increase in body condition, of approximately 1% of the lean body mass per day, indicating a shift towards fuelling for migratory flight. The overlap between stages of maintaining a stable and low body condition and moulting suggests a resource allocation towards feather growth before initiating fuelling. Our study describes moult strategy in Great Snipe conducted on their breeding grounds, highlighting the intrasexual differences, likely resulting from different parental duties of males and females of this lekking species.
Dynamic minimization of proteome reallocation explains metabolic transition in hierarchical utilization of mixed carbon sources

Cells choose between alternative pathways in metabolic networks under diverse environmental conditions, but the principles governing the choice are insufficiently understood, especially in response to dynamically changing conditions. Here we observed that a lactic acid bacterium Bacillus coagulans displayed homolactic fermentation on glucose or trehalose as the sole carbon source, but transitioned from homolactic to heterolactic fermentation during the hierarchical utilization of glucose and trehalose when growing on the mixture. We simulated the observation by dynamic minimization of reallocation of proteome (dMORP) using an enzyme-constrained genome-scale metabolic model of B. coagulans, which coincided with our multi-omics data. Moreover, we evolved strains to co-utilize mixed carbon sources, circumventing the hierarchical utilization and inactivating the choice of heterolactic fermentation. Altogether, the findings suggest that upon rapid environmental changes bacteria tend to minimize proteome reallocation and accordingly adjust metabolism, and dMORP would be useful in simulating and understanding cellular dynamics.
Long-read transcriptome sequencing of CLL and MDS patients uncovers molecular effects of SF3B1 mutations

Background Mutations in splicing factor 3B subunit 1 (SF3B1) frequently occur in patients with chronic lymphocytic leukemia (CLL) and myelodysplastic syndromes (MDS). These mutations have a different effect on the disease prognosis with beneficial effect in MDS and worse prognosis in CLL patients. A full-length transcriptome approach can expand our knowledge on SF3B1 mutation effects on RNA splicing and its contribution to patient survival and treatment options. Results We applied long-read transcriptome sequencing to 44 MDS and CLL patients with and without SF3B1 mutations and found > 60% of novel isoforms. Splicing alterations were largely shared between cancer types and specifically affected the usage of introns and 3’ splice sites. Our data highlighted a constrained window at canonical 3’ splice sites in which dynamic splice site switches occurred in SF3B1-mutated patients. Using transcriptome-wide RNA binding maps and molecular dynamics simulations, we showed multimodal SF3B1 binding at 3’ splice sites and predicted reduced RNA binding at the second binding pocket of SF3B1K700E. Conclusions Our work presents the hitherto most complete long-read transcriptome sequencing study in CLL and MDS and provides a resource to study aberrant splicing in cancer. Moreover, we showed that different disease prognosis results most likely from the different cell types expanded during cancerogenesis rather than different mechanism of action of the mutated SF3B1. These results have important implications for understanding the role of SF3B1 mutations in hematological malignancies and other related diseases. Highlights Long-read transcriptome sequencing data enables the identification of > 60% of novel isoforms in the transcriptomes of CLL and MDS patients and isogenic cell lines. SF3B1 mutations trigger common splicing alterations upon SF3B1 mutations across patient cohorts, most frequently decreased intron retention and increased alternative 3’ splice site usage. Mutation effect depends on alternative 3’ splice site and branch point positioning that coincide with bimodal SF3B1 binding at these sites Molecular dynamics simulations predict reduced binding of SF3B1K700E to mRNA at the second binding pocket harboring the polypyrimidine tract.
Different fluorescent labels report distinct components of spHCN channel voltage sensor movement

Voltage clamp fluorometry was used to probe the S4 helix movement in the voltage sensing domain of the sea urchin HCN channel expressed in Xenopus oocytes. Markedly different fluorescence responses were obtained with either ALEXA-488 or MTS-TAMRA covalently linked to Cys332 at the N-terminal end of S4. With hyperpolarizing steps, ALEXA-488 fluorescence increased rapidly showing characteristics consistent with it reporting the initial inward movement of S4 in agreement with previous studies. In contrast, MTS-TAMRA fluorescence was slower and correlated with the early phase of channel opening. In addition, a slow fluorescence component was resolved with both labels that tracked the development of the mode shift or channel hysteresis. This was quantitated as an increased deactivation tail current delay with concomitantly longer activation periods and was found to depend strongly on the presence of K+ ions in the pore. This indicated that the microenvironment of the fluorescent probes attached to Cys332 was strongly influenced by conformational changes in the pore domain. Collisional quenching experiments established that ALEXA-488 was more exposed to solvent than MTS-TAMRA. This was supported by structural predictions based on homology modelling of spHCN in the closed and open conformations with covalently linked fluorophores. This study demonstrates that components of S4 movement during channel activation can be kinetically resolved using different fluorescent probes to reveal three distinct biophysical properties: voltage-sensor movement, early channel opening and mode-shift. These data support the use of different labelling probes to interrogate distinct biophysical aspects of voltage-gated membrane proteins. Summary Voltage clamp fluorometry was used to probe the S4 helix movement in the voltage sensing domain of the spHCN channel expressed in Xenopus oocytes, labeled with either ALEXA-488 or MTS-TAMRA. Each fluorophore reported different components of S4 movement.
Large-Scale COI Gene Data Analysis for the Development of a Gel-Based Multiplex PCR Test Capable of Identifying Biting Midge Vector Species and Haplotypes (Diptera: Ceratopogonidae) of the Culicoides subgenus Avaritia Fox, 1955

The emergence of culicoid-transmitted bluetongue and Schmallenberg viruses in several European countries demonstrated the ability of indigenous biting midge species to transmit pathogens. Entomologic research programmes identified members of the Obsoletus Group (Culicoides subgenus Avaritia) as keyplayers in disease epidemiology in Europe. However, morphological identification of potential vectors to species level is challenging due to the existence of isomorphic species. PCR tests developed to facilitate genetic species determination have been dismantled by the discovery of new genetic variants (haplotypes) of C. obsoletus sensu stricto (s.s.), forming distinct clades. In this study, 4,422 GenBank entries of the mitochondrial cytochrome c oxidase subunit I (COI) gene of subgenus Avaritia members of the genus Culicoides were analyzed to develop a conventional multiplex PCR, capable of detecting all vector species and recently described clades of the western Palaearctic in this subgenus. Numerous GenBank entries incorrectly assigned to a species were identified, analyzed and reassigned. The results suggest that the three clades of C. obsoletus represent independent species, whereas C. montanus should rather be regarded as a genetic variant of C. obsoletus s.s. Based on these findings, specific primers were designed and validated with DNA material from field-caught biting midges which achieved very high diagnostic sensitivity (100%) when compared to an established reference PCR (82.6%). Hence, the newly developed multiplex PCR represents the first molecular tool which enables both the identification of accepted species and of the three clades of C. obsoletus s.s. and could provide new insights into the ecology of the latter.
CDK-4 regulates nucleolar size and metabolism at the cost of late-life fitness in C. elegans

An outstanding question in biology concerns mechanisms of size control in organs, cells, and organelles. Size impacts metabolic efficiency, surface area-to-volume ratio, and environmental adaptation, which are all required for optimal function. Cyclin-dependent kinase 4 (CDK4), traditionally recognized for its role in cell cycle progression, has gained increasing support for cell cycle-independent roles. Previously, we described a mechanism of cell size control involving a CDK4 and p38 MAPK circuitry that dictates target cell size. In this study, we target the CDK4/6 ortholog CDK-4 in the nematode worm Caenorhabditis elegans to describe functional consequences of changing biological size in vivo. Our data suggest that CDK-4 regulates nucleolar size and anabolic metabolism independent from cell cycle progression. When size and metabolism are increased, we report enhanced thermotolerance early in life but accelerated aging and reduced longevity late in life, suggesting a novel function of CDK-4 in somatic maintenance and organism health.
CDX2 dose-dependently influences the gene regulatory network underlying human extraembryonic mesoderm development

Proper regulation of gene dosage is critical for the development of the early embryo and the extraembryonic tissues that support it. Specifically, loss of Cdx2 in vivo leads to stunted development of the allantois, an extraembryonic mesoderm-derived structure critical for nutrient delivery and waste removal in the early embryo. In this study, we investigate how CDX2 dose-dependently influences the gene regulatory network underlying extraembryonic mesoderm development. We generate an allelic series for CDX2 in human induced pluripotent stem cells (hiPSCs) consisting of WT, heterozygous, and homozygous null CDX2 genotypes, differentiate these cells in a 2D gastruloid model, and subject these cells to multiomic single nucleus RNA and ATAC sequencing. We identify several genes that CDX2 dose-dependently regulate cytoskeletal integrity and adhesiveness in the extraembryonic mesoderm population, including regulators of the VEGF, canonical WNT, and non-canonical WNT signaling pathways. Despite these dose-dependent gene expression patterns, snATAC-seq reveals that heterozygous CDX2 expression is capable of inducing a WT-like chromatin accessibility profile, suggesting accessibility is not sufficient to drive gene expression when the CDX2 dosage is reduced. Finally, because the loss of CDX2 or TBXT phenocopy one another in vivo, we compare differentially expressed genes in our CDX2 knock-out model to those from TBXT knock-out hiPSCs differentiated in an analogous experiment. This comparison identifies several communally misregulated genes that are critical for cytoskeletal integrity and tissue permeability, including ANK3 and ANGPT1. Together, these results clarify how CDX2 dose-dependently regulates gene expression in the extraembryonic mesoderm and suggest these genes may underlie the defects in vascular development and allantoic elongation seen in the absence or reduction of CDX2 in vivo. Summary Statement Using 2D human gastruloids, CDX2 is shown to dose-dependently influence genes related to tissue permeability, cell-cell adhesions, and cytoskeletal architecture during extraembryonic mesoderm development.