Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Cultural Commonsense Knowledge Distillation

Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents MANGO, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. We judiciously and iteratively prompt LLMs for this purpose from two entry points, concepts and cultures. Outputs are consolidated via clustering and generative summarization. Running the MANGO method with GPT-3.5 as underlying LLM yields 167K high-accuracy assertions for 30K concepts and 11K cultures, surpassing prior resources by a large margin. For extrinsic evaluation, we explore augmenting dialogue systems with cultural knowledge assertions. We find that adding knowledge from MANGO improves the overall quality, specificity, and cultural sensitivity of dialogue responses, as judged by human annotators. Data and code are available for download.
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection

Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount. In this work, we propose DELL that identifies three key stages in misinformation detection where LLMs could be incorporated as part of the pipeline: 1) LLMs could \emph{generate news reactions} to represent diverse perspectives and simulate user-news interaction networks; 2) LLMs could \emph{generate explanations} for proxy tasks (e.g., sentiment, stance) to enrich the contexts of news articles and produce experts specializing in various aspects of news understanding; 3) LLMs could \emph{merge task-specific experts} and provide an overall prediction by incorporating the predictions and confidence scores of varying experts. Extensive experiments on seven datasets with three LLMs demonstrate that DELL outperforms state-of-the-art baselines by up to 16.8\% in macro f1-score. Further analysis reveals that the generated reactions and explanations are greatly helpful in misinformation detection, while our proposed LLM-guided expert merging helps produce better-calibrated predictions.
Properties and Challenges of LLM-Generated Explanations

The self-rationalising capabilities of large language models (LLMs) have been explored in restricted settings, using task/specific data sets. However, current LLMs do not (only) rely on specifically annotated data; nonetheless, they frequently explain their outputs. The properties of the generated explanations are influenced by the pre-training corpus and by the target data used for instruction fine-tuning. As the pre-training corpus includes a large amount of human-written explanations"in the wild", we hypothesise that LLMs adopt common properties of human explanations. By analysing the outputs for a multi-domain instruction fine-tuning data set, we find that generated explanations show selectivity and contain illustrative elements, but less frequently are subjective or misleading. We discuss reasons and consequences of the properties' presence or absence. In particular, we outline positive and negative implications depending on the goals and user groups of the self-rationalising system.
Stressed and sliding ice surfaces liquefy without much heating

The low kinetic friction observed between ice or snow and numerous counterbodies is commonly attributed to a thin interfacial water layer [1-3], which is believed to exist because of pressure melting [4], surface melting [5, 6], or friction-induced heating [7]. However, even the currently leading theory of frictional melting keeps being challenged, for example, due to the lack of detectable warming of snow surfaces under a rotating slider at -7{\deg}C temperature and 1 m/s sliding velocity despite high temporospatial resolution [8]. Here we present molecular simulations of ice interfaces that reveal that ice surfaces liquefy readily without melting thermally but rather by displacement driven amorphization, normal-stress gradients, and tensile in-plane stress. Yet, friction coefficients below 0.01, as observed during the sliding of hydrophobic solids over ice [9], appear possible only when the counterfaces are smooth and allow water to slip past them. Our findings provide fundamental guidelines on how to optimize ice friction and challenge experimentalists to measure the surface temperature of ice and snow at minute scales and with unprecedented speed.
The Mean Field Market Model Revisited

In this paper, we present an alternative perspective on the mean-field LIBOR market model introduced by Desmettre et al. in arXiv:2109.10779. Our novel approach embeds the mean-field model in a classical setup, but retains the crucial feature of controlling the term rate's variances over large time horizons. This maintains the market model's practicability, since calibrations and simulations can be carried out efficiently without nested simulations. In addition, we show that our framework can be directly applied to model term rates derived from SOFR, ESTR or other nearly risk-free overnight short-term rates -- a crucial feature since many IBOR rates are gradually being replaced. These results are complemented by a calibration study and some theoretical arguments which allow to estimate the probability of unrealistically high rates in the presented market models.
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models' knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential.
Moving and fusion of Majorana zero modes in the presence of nonadiabatic transitions

We perform simulations for moving and non-Abelian fusion of Majorana zero modes in topological superconducting quantum wires. We display interesting behaviors of nonadiabatic transition associated with the moving through mini-gate-controlled multiple-segments modulations. Owing to breaking of the initial fermion parity induced by nonadiabatic transitions, deviation from the standard fusion rule is analyzed. Moreover, we develop a measurement scheme to infer the amount of fermion parity breaking and nonadiabatic transition probability to excited states, based on the characteristic spectrum of measurement current by a uantum-point-contact detector, in measuring the charge occupation dynamics in a fusion-outcome-probing quantum dot.
New Chinese Facilities for Short-Range Correlation Physics

This article explores the significant advancements in Short-Range Correlation (SRC) research enabled by the latest Chinese nuclear physics facilities- CSR at HIRFL, HIAF, SHINE, and the upcoming EicC. These facilities introduce cutting-edge technologies and methodologies, addressing existing challenges and broadening the scope for SRC studies. By providing detailed insights into the capabilities and expected contributions of each facility, the paper highlights China's emerging role in the global nuclear physics landscape. The collaborative potential, alongside complementary global efforts, positions these facilities to deeply influence our understanding of nuclear matter's fundamental properties and interactions.
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative LLM Inference

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This results in increased inference time and costs. Cross-lingual vocabulary adaptation methods have been proposed for adapting models to a target language aiming to improve downstream performance. However, the effectiveness of these methods on increasing inference efficiency of generative LLMs has yet to be explored. In this paper, we perform an empirical study of various cross-lingual vocabulary adaptation methods on five generative LLMs (including monolingual and multilingual models) across four typologically-diverse languages and four natural language understanding tasks. We find that cross-lingual vocabulary adaptation substantially contributes to LLM inference speedups of up to 271.5%. We also show that adapting LLMs that have been pre-trained on more balanced multilingual data results in downstream performance comparable to the original models.
Goal-Conditioned Offline Reinforcement Learning via Metric Learning

In this work, we address the problem of learning optimal behavior from sub-optimal datasets in the context of goal-conditioned offline reinforcement learning. To do so, we propose a novel way of approximating the optimal value function for goal-conditioned offline RL problems under sparse rewards, symmetric and deterministic actions. We study a property for representations to recover optimality and propose a new optimization objective that leads to such property. We use the learned value function to guide the learning of a policy in an actor-critic fashion, a method we name MetricRL. Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets. Moreover, we show the effectiveness of our method in dealing with high-dimensional observations and in multi-goal tasks.
Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions.
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data

Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Although significant research efforts have been devoted to addressing these challenges independently, their synthesis remains less explored. In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously. We theoretically quantify the privacy guarantee through the lens of the emerging f-differential privacy (DP) and the Byzantine resilience of the proposed algorithm. Particularly, in terms of privacy guarantees, compared to the existing sign-based approach StoSign, the proposed method improves the dimension dependence on the gradient size and enjoys privacy amplification by mini-batch sampling while ensuring a comparable convergence rate. We also prove that TernaryVote is robust when less than 50% of workers are blind attackers, which matches that of SIGNSGD with majority vote. Extensive experimental results validate the effectiveness of the proposed algorithm.
Community ecology of aquatic insects in forested headwater streams in the southern Appalachians

Competing paradigms of community assembly emphasize different mechanisms for predicting patterns in biogeography. Niche assembly emphasizes the role of environmental gradients as filters that organize a metacommunity by locally selecting colonizers with similar functional traits, whereas dispersal assembly emphasizes the importance of source pool characteristics and dispersal limitation in organizing a metacommunity. In this study, I developed a framework that uses spatially explicit patterns in taxonomic and functional measures of community composition as diagnostics for community assembly processes for benthic macroinvertebrates in headwater streams in the southern Appalachians. Distance decay in taxonomic and functional similarity was used to determine the scales at which taxonomic turnover occurred within functional niches. Trait-neutral models of community composition were used as null models to assess which functional traits were the best candidates to explain how community composition was influenced by environmental gradients: an assessment of niche-based community assembly. Regional scale patterns suggested that niche-based community assembly was the dominant mechanism organizing community composition in headwater streams at local scales (<30km). Therefore, I compared how well trait-neutral models identified functional traits as relevant to community sorting against how well observed trait distributions correlated with environmental variation at a local scale, in the Ray Branch catchment (<10km study extent). Functional traits exhibiting non-random distributions within the Ray Branch watershed were most strongly correlated with environmental variation. Lastly, I assessed how the influences of niche and dispersal assembly on benthic macroinvertebrate community composition were affected by disturbance (shelterwood logging). Environmental variables defining the habitat template, and macroinvertebrate community composition, were measured before and after the disturbance; and path analysis was used to quantify the disturbance effect. The relationship between environmental variation and functional composition increased
I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench

Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this paper, we introduce AwareBench, a benchmark designed to evaluate awareness in LLMs. Drawing from theories in psychology and philosophy, we define awareness in LLMs as the ability to understand themselves as AI models and to exhibit social intelligence. Subsequently, we categorize awareness in LLMs into five dimensions, including capability, mission, emotion, culture, and perspective. Based on this taxonomy, we create a dataset called AwareEval, which contains binary, multiple-choice, and open-ended questions to assess LLMs' understandings of specific awareness dimensions. Our experiments, conducted on 13 LLMs, reveal that the majority of them struggle to fully recognize their capabilities and missions while demonstrating decent social intelligence. We conclude by connecting awareness of LLMs with AI alignment and safety, emphasizing its significance to the trustworthy and ethical development of LLMs. Our dataset and code are available at https://github.com/HowieHwong/Awareness-in-LLM.
Gravitational Waves: Echoes of the Biggest Bangs since the Big Bang and/or BSM Physics?

"If one could ever prove the existence of gravitational waves, the processes responsible for their generation would probably be much more curious and interesting than even the waves themselves. "(Gustav Mie, 1868 - 1957) The discovery of gravitational waves has opened new windows on astrophysics, cosmology and physics beyond the Standard Model (BSM). Measurements by the LIGO, Virgo and KAGRA Collaborations of stellar-mass binaries and neutron star mergers have shown that gravitational waves travel at close to the velocity of light, and also constrain BSM possibilities such as a graviton mass and Lorentz violation in gravitational wave propagation. Follow-up measurements of neutron star mergers have provided evidence for the production of heavy elements, possibly including some essential for human life. The gravitational waves in the nanoHz range observed by Pulsar Timing Arrays (PTAs) may have been emitted by supermassive black hole binaries, but might also have originated from BSM cosmological scenarios such as cosmic strings, or phase transitions in the early Universe. The answer to the question in the title may be provided by gravitational-wave detectors at higher frequencies, such as LISA and atom interferometers.
Herbarium specimens reveal links between Capsella bursa-pastoris leaf shape and climate

Studies into the evolution and development of leaf shape have connected variation in plant form, function, and fitness. For species with consistent leaf margin features, patterns in leaf architecture are related to both biotic and abiotic factors. However, for species with inconsistent leaf margin features, quantifying leaf shape variation and the effects of environmental factors on leaf shape has proven challenging. To investigate leaf shape variation in species with inconsistent shapes, we analyzed approxi-mately 500 digitized Capsella bursa-pastoris specimens collected throughout the continental U.S. over a 100-year period with geometric morphometric modeling and deterministic techniques. We generated a morphospace of C. bursa-pastoris leaf shapes and modeled leaf shape as a function of environment and time. Our results suggest C. bursa-pastoris leaf shape variation is strongly associated with temperature over the C. bursa-pastoris growing season, with lobing decreasing as temperature increases. While we expected to see changes in variation over time, our results show that level of leaf shape variation is consistent over the 100-year period. Our findings showed that species with inconsistent leaf shape variation can be quantified using geometric morphometric modeling techniques and that temperature is the main environmental factor influencing leaf shape variation.
Brain-handedness associations depend on how and when handedness is measured

Hand preference is ubiquitous, intuitive, and often simplified to right- or left-handed. Accordingly, differences between right- and left-handed individuals in the brain have been established. Nevertheless, considering handedness as a binarized construct fails to capture the variability of brain-handedness associations across different domains or activities. Further, many cultures, environments, and generations impose right-handed norms, and handedness preferences can change over the lifespan. As a result, brain-handedness associations may depend on how and when handedness is measured. We used two large datasets, the Human Connectome Project-Development (HCP-D; n=465; age=5-21 years) and Human Connectome Project-Aging (HCP-A; n=368; age=36-100 years), to explore handedness preferences and brain-handedness associations. Nine items from the Edinburgh Handedness Inventory were associated with resting-state functional connectomes. We show that brain-handedness associations differed across the two cohorts. Moreover, these differences depended on the way handedness was measured. Given that brain-handedness associations differ across handedness measures and datasets, we caution against a one-size-fits-all approach to neuroimaging studies of this complex trait.
ChemGLaM: Chemical Genomics Language Models for Compound-Protein Interaction Prediction

Accurate prediction of compound-protein interaction (CPI) is of great importance for drug discovery. For creating generalizable CPI prediction deep learning (DL) models, the expansion of CPI data through experimental validation is crucial. However, the cost associated with these experimental validations is a bottleneck. Recently developed large language models (LLMs) such as chemical language models (CLMs) and protein language models (PLMs) have emerged as foundation models, demonstrating high generalization performance in various tasks involving compounds and proteins. Inspired by this, we propose a chemical genomics language model, ChemGLaM, for predicting compound-protein interactions. ChemGLaM is based on the 2 independent language models, MoLFormer for compounds and ESM-2 for proteins, and fine-tuned for the CPI datasets using an interaction block with a cross-attention mechanism. ChemGLaM is capable of predicting interactions between unknown compounds and proteins with higher accuracy than existing CPI prediction models, demonstrating that combining the independently pre-trained foundation models is effective for obtaining sophisticated representation of compound-protein interactions. Furthermore, visualizing the learned cross-attention map can offer explainable insights into the mechanism of compound-protein interaction. This study emphasizes the potential of integrating the independent foundation models for the tasks of multi-modality such as CPI prediction.
UBA1-CDK16: A Sex-Specific Chimeric RNA and Its Role in Immune Sexual Dimorphism

RNA processing mechanisms, such as alternative splicing and RNA editing, have been recognized as critical means to expand the transcriptome. Chimeric RNAs formed by intergenic splicing provide another potential layer of RNA diversification. By analyzing a large set of RNA-Seq data and validating results in over 1,200 blood samples, we identified UBA1-CDK16, a female-specific chimeric transcript. Intriguingly, both parental genes, are expressed in males and females. Mechanistically, UBA1-CDK16 is produced by cis-splicing between the two adjacent X-linked genes, originating from the inactive X chromosome. A female-specific chromatin loop, formed between the junction sites, facilitates the alternative splicing of its readthrough precursor. This unique chimeric transcript exhibits evolutionary conservation, evolving to be female-specific from non-human primates to humans. Furthermore, our investigation reveals that UBA1-CDK16 is enriched in the myeloid lineage and plays a regulatory role in myeloid differentiation. Notably, female COVID-19 patients who tested negative for this chimeric transcript displayed higher counts of neutrophils, highlighting its potential role in disease pathogenesis. These findings support the notion that chimeric RNAs represent a new repertoire of transcripts that can be regulated independently from the parental genes, and a new class of RNA variance with potential implications in sexual dimorphism and immune responses.