Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Inference of Retrieval and Generation for Passage Re-ranking

Passage retrieval is a crucial component of modern open-domain question answering (QA) systems, providing information for downstream QA components to generate accurate and transparent answers. In this study we focus on passage re-ranking, proposing a simple yet effective method, Joint Passage Re-ranking (JPR), that optimizes the mutual information between query and passage distributions, integrating both cross-encoders and generative models in the re-ranking process. Experimental results demonstrate that JPR outperforms conventional re-rankers and language model scorers in both open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.
Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis

Analyses for linking language with psychological factors or behaviors predominately treat linguistic features as a static set, working with a single document per person or aggregating across multiple posts (e.g. on social media) into a single set of features. This limits language to mostly shed light on between-person differences rather than changes in behavior within-person. Here, we collected a novel dataset of daily surveys where participants were asked to describe their experienced well-being and report the number of alcoholic beverages they had within the past 24 hours. Through this data, we first build a multi-level forecasting model that is able to capture within-person change and leverage both the psychological features of the person and daily well-being responses. Then, we propose a longitudinal version of differential language analysis that finds patterns associated with drinking more (e.g. social events) and less (e.g. task-oriented), as well as distinguishing patterns of heavy drinks versus light drinkers.
Probing of pretrained multilingual models on the knowledge of discourse

With the raise of large language models (LLMs), different evaluation methods, including probing methods, are gaining more attention. Probing methods are meant to evaluate LLMs on their linguistic abilities. However, most of the studies are focused on morphology and syntax, leaving discourse research out of the scope. At the same time, understanding discourse and pragmatics is crucial to building up the conversational abilities of models. In this paper, we address the problem of probing several models of discourse knowledge in 10 languages. We present an algorithm to automatically adapt existing discourse tasks to other languages based on the Universal Dependencies (UD) annotation. We find that models perform similarly on high- and low-resourced languages. However, the overall low performance of the models’ quality shows that they do not acquire discourse well enough.
Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization

We propose a method that integrates supervised extractive and generative language models for providing supporting evidence of suicide risk in the CLPsych 2024 shared task. Our approach comprises three steps. Initially, we construct a BERT-based model for estimating sentence-level suicide risk and negative sentiment. Next, we precisely identify high suicide risk sentences by emphasizing elevated probabilities of both suicide risk and negative sentiment. Finally, we integrate generative summaries using the MentaLLaMa framework and extractive summaries from identified high suicide risk sentences and a specialized dictionary of suicidal risk words. SophiaADS, our team, achieved 1st place for highlight extraction and ranked 10th for summary generation, both based on recall and consistency metrics, respectively.
Convective Cores in Continental and Oceanic Thunderstorms: Strength, Width, and Dynamics

ii Acknowledgments. iii Vita . iv List of Tables . vi List of Figures . vvii List of Abbreviations . x List of Constants and Variables . xi Chapter 1: Introduction . 1 Chapter 2: Datasets Gathered for Analysis . 17 Chapter 3: Methodology . 26 Chapter 4: Analysis of Aircraft Penetration Data . 30 Chapter 5: Analysis of Oceanic Soundings . 63 Chapter 6: Conclusions . 77 References . 81 Appendix A: Updraft Selection Program . 86 Appendix B: CAPE Calculation Program. 89
Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at CASE 2024

Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants’ performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44% in subtask C, 78.58% in subtask B, and 74.83% in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition’s leaderboard.
Rainbow - A Benchmark for Systematic Testing of How Sensitive Visio-Linguistic Models are to Color Naming

With the recent emergence of powerful visio-linguistic models comes the question of how fine-grained their multi-modal understanding is. This has lead to the release of several probing datasets. Results point towards models having trouble with prepositions and verbs, but being relatively robust when it comes to color.To gauge how deep this understanding goes, we compile a comprehensive probing dataset to systematically test multi-modal alignment around color. We demonstrate how human perception influences descriptions of color and pay special attention to the extent to which this is reflected within the predictions of a visio-linguistic model. Probing a set of models with diverse properties with our benchmark confirms the superiority of models that do not rely on pre-extracted image features, and demonstrates that augmentation with too much noisy pre-training data can produce an inferior model. While the benchmark remains challenging for all models we test, the overall result pattern suggests well-founded alignment of color terms with hues. Analyses do however reveal uncertainty regarding the boundaries between neighboring color terms.
Experimenting with Discourse Segmentation of Taiwan Southern Min Spontaneous Speech

Discourse segmentation received increased attention in the past years, however the majority of studies have focused on written genres and with high-resource languages. This paper investigates discourse segmentation of a Taiwan Southern Min spontaneous speech corpus. We compare the fine-tuning a Language Model (LLM using two approaches: supervised, thanks to a high-quality annotated dataset, and weakly-supervised, requiring only a small amount of manual labeling. The corpus used here is transcribed with both Chinese characters and romanized transcription. This allows us to compare the impact of the written form on the discourse segmentation task. Additionally, the dataset includes manual prosodic breaks labeling, allowing an exploration of the role prosody can play in contemporary discourse segmentation systems grounded in LLMs. In our study, the supervised approach outperforms weak-supervision ; character-based version demonstrated better scores compared to the romanized version; and prosodic information proved to be an interesting source to increase discourse segmentation performance.
Graph Contrastive Learning of Subcellular-resolution Spatial Transcriptomics Improves Cell Type Annotation and Reveals Critical Molecular Pathways

Imaging based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation approaches of iST primarily utilize gene expression information while neglecting the spatial distribution of RNAs within cells. In this work, we introduce a semi-supervised graph contrastive learning method called Focus, the first method, to the best of our knowledge, that explicitly models RNA’s subcellular distribution and community to improve cell type annotation. Focus first constructs gene neighborhood networks based on the subcellular colocalization relationship of RNA transcripts. Next the subcellular graph of each cell can be augmented by adding important edges and nodes or removing trivial edges and nodes. Focus then aims to maximize the similarity between positive pairs from two augmented views of the same cell and minimize the similarity between negative pairs from different cells within a common batch. Guided by a limited amount of labeled data, Focus is capable of assigning cell type identities for the entire datasets at high accuracy. Extensive experiments demonstrate effectiveness of Focus compared to existing state-of-the-art approaches across a range of spatial transcriptomics platforms and biological systems. Furthermore, Focus enjoys the advantages of revealing intricate cell type-specific subcellular spatial gene patterns and providing interpretable subcellular gene analysis, such as defining the gene importance score. Importantly, with the importance score, Focus identifies genes harboring strong relevance to cell type-specific pathways, indicating its potential in uncovering novel regulatory programs across numerous biological systems. Focus is freely accessible at https://github.com/OmicsML/focus.
Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subregions of VFC in macaque monkeys performing a two-choice probabilistic task for different fruit juices outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral subdivision 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within 12o, by contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subregions, while the properties of the reward, such as its flavor, were more strongly represented in areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates. SIGNIFICANCE STATEMENT Ventral frontal cortex (VFC) is essential for flexible decision-making and is composed of many anatomically defined subdivisions. How neural representations related to decision-making vary or not between these subdivisions is unclear. Here we recorded single neuron activity from eight anatomically distinct subdivisions of VFC while macaques made choices between stimuli based on the probability of receiving different flavored fruit juices. We report that neural representations across these subdivisions were dissociable. Area 12l exhibiting the most integrated representations of decision variables at the level of single neurons. By contrast, activity in area 12o was closely related to reward probability whereas activity in area 11m/l and 13m represented juice flavor. Thus, neural representations are distinct across anatomically separable parts of VFC.
Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty and speed

Decision-making is often accompanied by a level of confidence regarding the accuracy of one’s decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence – generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT have similar neural correlates. Within a perceptual decision-making task, we applied fMRI-informed EEG-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted parietal cortical layer-based, distributed and nonlinear coding of RT, confidence or uncertainty, which cannot be readily captured by scalp-level EEG. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.
A novel regulatory pathway recognizes and degrades transcripts with long 3′ untranslated regions

Quantitative control of gene expression is fundamental to cellular function, and post-transcriptional regulation is a consequential, additional layer of control over protein output. Within mammalian mRNAs, the 3′ untranslated region (3′UTR) acts as a hub of regulatory control, typically mediated by regulatory sequence elements within the 3′UTR. We have found that expression from transcripts with long 3′UTRs is strongly repressed compared to those with short 3′UTRs, and this phenomenon appears to be independent of sequence elements within the 3′UTR. The repression increases with 3′UTR length and is substantial; reporters with 2,000 nucleotide long 3′UTRs are repressed >10-fold compared to 400 nucleotide 3′UTRs. Conversely, increasing the length of the coding region has no effect on expression, demonstrating that it is 3′UTR length, not transcript length, that elicits repression. Reporters with different length 3′UTRs show no difference in transcription rates nor translation efficiency but have clear differences in mRNA half-lives and nucleocytoplasmic distribution, indicating repression of long-3′UTR transcripts is mediated by accelerated RNA decay. However, this transcript degradation does not involve the nonsense-mediated decay (NMD) pathway nor other canonical RNA decay pathways. In order to identify trans factors involved in 3′UTR-length-mediated decay, we quantified the differences in proteins bound to reporters with long and short 3′UTRs, and identified over 100 differentially associated factors. Knockdowns of two differentially associating proteins, the mRNA nuclear export factors DDX39B and ZC3H11A, indicate they play a role in repression of mRNAs with long 3′UTRs. This study establishes a novel length-dependent regulatory feature of 3′UTRs, which potentially regulates many genes.
Post-Translational Modifications Control Phase Transitions of Tau

The self-assembly of Tau(297-391) into filaments, which mirror the structures observed in Alzheimer’s disease (AD) brains, raises questions about the role of AD-specific post-translational modifications (PTMs) in the formation of paired helical filaments (PHFs). To investigate this, we developed a synthetic approach to produce Tau(291-391) featuring N-acetyllysine, phosphoserine, phosphotyrosine, and N-glycosylation at positions commonly modified in post-mortem AD brains, thus facilitating the study of their roles in Tau pathology. Using transmission electron microscopy (TEM), cryo-electron microscopy (cryo-EM), and a range of optical microscopy techniques, we discovered that these modifications generally hinder the in vitro assembly of Tau into PHFs. Interestingly, while acetylation’s effect on Tau assembly displayed variability, either promoting or inhibiting phase transitions in the context of cofactor free aggregation, heparin-induced aggregation, and RNA-mediated liquid-liquid phase separation (LLPS), phosphorylation uniformly mitigated these processes. Our observations suggest that PTMs, particularly those situated outside the fibril’s rigid core are pivotal in the nucleation of PHFs. Moreover, in scenarios involving heparin-induced aggregation leading to the formation of heterogeneous aggregates, most AD-specific PTMs, except for K311, appeared to decelerate the aggregation process. The impact of acetylation on RNA-induced LLPS was notably site-dependent, exhibiting both facilitative and inhibitory effects, whereas phosphorylation consistently reduced LLPS across all proteoforms examined. These insights underscore the complex interplay between site-specific PTMs and environmental factors in modulating Tau aggregation kinetics, enhancing our understanding of the molecular underpinnings of Tau pathology in AD and highlighting the critical role of PTMs located outside the ordered filament core in driving the self-assembly of Tau into PHF structures.
CDK4 and CDK6 upregulation promotes DNA replication stress, genomic instability and resistance to EGFR targeted therapy in lung cancer

Genetic interactions impact both normal human physiology and human diseases, such as cancer. Here, we study genetic interactions through the lens of human lung cancers driven by oncogenic forms of the epidermal growth factor receptor (EGFR), which we and others previously showed harbor a rich landscape of genetic co-alterations and potential genetic interactions. Among the most common genetic co-alterations with oncogenic EGFR are genomic amplifications of cell cycle regulators CDK4 or CDK6, which have been implicated in EGFR inhibitor clinical resistance, although the mechanism underlying this effect is not well characterized. We show that CDK4/6 upregulation overcomes EGFR inhibitor-induced G1/S cell cycle arrest in association with increased replication stress, DNA damage and genomic instability. These biological effects arising in CDK4/6 upregulated tumors help to enable resistance to EGFR targeted therapies through established genetic resistance mechanisms. Combinatorial EGFR and CDK4/6 inhibitor treatment alleviated genomic instability and EGFR inhibitor resistance in patient-derived preclinical models. This study reveals mechanistic and clinical impacts of the genetic interaction between oncogenic EGFR and CDK4/6 co-alterations in human lung cancer.
A randomized, controlled, two-center preclinical trial assessing the efficacy of a new benzodiazepine–dihydropyridine hybrid molecule (JM-20) in rodent models of ischemic stroke

JM-20 is a novel multifunctional benzodiazepine molecule with potent neuroprotective effects in rat focal cerebral ischemia. To confirm previous results obtained in single laboratories with small sample sizes, and to provide a robust preclinical evidence base for potential clinical development in stroke, we have performed a two-center preclinical trial with sufficiently large group sizes to detect relevant effects, minimizing biases in experimental design as much as possible (randomization, blinding, predefined in- and exclusion criteria) and increasing external and construct validities by performing experimental focal cerebral ischemia by different surgeons in two different laboratories on two continents, including two species (480 mice and 55 rats), different suppliers, young, young adult, and mature adult animals (range 2 -16 months) as well as comorbid animals (diabetes). While JM-20 improved functional outcomes after middle cerebral artery occlusion in young adult mice at day 7 and appeared to reduce mortality (not statistically significant), it had no effect in mature adult or comorbid (STZ-induced diabetes) mice. Effect sizes, where statistically significant, were modest, and much lower than those reported in the previous studies. Meta-analysis of all individual mouse data did not reveal statistically significant different functional outcomes or mortalities between vehicle- and JM-20-treated animals, although neuroscores and survival were slightly better in JM-20-treated animals. In the less severe model of permanent cortical focal cerebral ischemia in rats, JM-20 significantly reduced brain infarction. We conclude that we were able to confirm the neuroprotective potential of JM-20. However, effect sizes were substantially lower as previously described in small, monocentric trials. Further study is needed to determine whether JM-20 could be effective in less severe cases of focal cerebral ischemia or when used in combination with thrombolysis.
Directional swimming patterns in jellyfish aggregations

Having a profound influence on marine and coastal environments worldwide, jellyfish hold significant scientific, economic, and public interest. The predictability of outbreaks and dispersion of jellyfish is limited by a fundamental gap in our understanding of their movement. Although there is evidence that jellyfish may actively affect their position, the role of active swimming in controlling jellyfish movement, and the characteristics of jellyfish swimming behavior, are not well understood. Consequently, jellyfish are often regarded as passively drifting or randomly moving organisms, both conceptually and in process studies. Here we show that the movement of jellyfish is controlled by distinctly directional swimming patterns, which are oriented against the direction of surface gravity waves. Taking a Lagrangian viewpoint from drone videos that allows the tracking of multiple adjacent jellyfish, and focusing the scyphozoan jellyfish Rhopilema nomadica as a model organism, we show that the behavior of individual jellyfish translates into a synchronized directional swimming of the aggregation as a whole. Numerical simulations show that this counter-wave swimming behavior results in biased correlated random-walk movement patterns that reduce the risk of stranding, thus providing jellyfish with an adaptive advantage critical to their survival. Our results emphasize the importance of active swimming in regulating jellyfish movement, and open the way for a more accurate representation in model studies, thus improving the predictability of jellyfish outbreaks and their dispersion, and contributing to our ability to mitigate their possible impact on coastal infrastructure and populations.