InfoPattern: Unveiling Information Propagation Patterns in Social Media

Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation. Our demo InfoPattern centers on the interplay between language and human ideology. The demo (Code: https://github.com/blender-nlp/InfoPattern ) is capable of: (1) red teaming to simulate adversary responses from opposite ideology communities; (2) stance detection to identify the underlying political sentiments in each message; (3) information propagation graph discovery to reveal the evolution of claims across various communities over time. (Live Demo: https://incas.csl.illinois.edu/blender/About

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity

Traditional computer vision models often require extensive manual effort for data acquisition, annotation and validation, particularly when detecting subtle behavioral nuances or events. The difficulty in distinguishing routine behaviors from potential risks in real-world applications, such as differentiating routine shopping from potential shoplifting, further complicates the process. Moreover, these models may demonstrate high false positive rates and imprecise event detection when exposed to real-world scenarios that differ significantly from the conditions of the training data. To overcome these hurdles, we present Ethosight, a novel zero-shot computer vision system. Ethosight initiates with a clean slate based on user requirements and semantic knowledge of interest. Using localized label affinity calculations and a reasoning-guided iterative learning loop, Ethosight infers scene details and iteratively refines the label set. Reasoning mechanisms can be derived from large language models like GPT4, symbolic reasoners like OpenNARS\cite{wang2013}\cite{wang2006}, or hybrid systems. Our evaluations demonstrate Ethosight's efficacy across 40 complex use cases, spanning domains such as health, safety, and security. Detailed results and case studies within the main body of this paper and an appendix underscore a promising trajectory towards enhancing the adaptability and resilience of computer vision models in detecting and extracting subtle and nuanced behaviors

Unique corrosion resistance of ultrahigh pressure Mg-25Al binary alloys.

Differing from as-cast and solid-solution alloys with coarse eutectic phases (Mg17Al12), a single-phase structure is attained in Mg-25wt.%Al alloy after ultrahigh-pressure solid-solution (USS, 800 oC, 4GPa). This USSed Mg-25wt.%Al sample exhibits a prominent age-hardening response due to the nano-scaled Mg17Al12 particles. Three testing methods confirm that USS-aged Mg-25wt.%Al alloy shows good corrosion resistance, which overwhelms the majority of Mg-based alloys reported so far, near to high purity Mg. The main reason is attributed to the formation of Al-rich oxide layer, wherein residual stress and pitting corrosion are eliminated. It provides a new avenue for developing corrosion resistant Mg alloys

Anisotropic Singlet Fission in Single Crystalline Hexacene

Singlet fission is known to improve solar energy utilization by circumventing the Shockley-Queisser limit. The two essential steps of singlet fission are the formation of a correlated triplet pair and its subsequent quantum decoherence. However, the mechanisms of the triplet pair formation and decoherence still remain elusive. Here we examined both essential steps in single crystalline hexacene and discovered remarkable anisotropy of the overall singlet fission rate along different crystal axes. Since the triplet pair formation emerges on the same timescale along both crystal axes, the quantum decoherence is likely responsible for the directional anisotropy. The distinct quantum decoherence rates are ascribed to the notable difference on their associated energy loss according to the Redfield quantum dissipation theory. Our hybrid experimental/theoretical framework will not only further our understanding of singlet fission, but also shed light on the systematic design of new materials for the third-generation solar cells

The role of the RACK1 ortholog Cpc2p in modulating pheromone-induced cell cycle arrest in fission yeast

The detection and amplification of extracellular signals requires the involvement of multiple protein components. In mammalian cells the receptor of activated C kinase (RACK1) is an important scaffolding protein for signal transduction networks. Further, it also performs a critical function in regulating the cell cycle by modulating the G1/S transition. Many eukaryotic cells express RACK1 orthologs, with one example being Cpc2p in the fission yeast Schizosaccharomyces pombe. In contrast to RACK1, Cpc2p has been described to positively regulate, at the ribosomal level, cells entry into M phase. In addition, Cpc2p controls the stress response pathways through an interaction with Msa2p, and sexual development by modulating Ran1p/Pat1p. Here we describe investigations into the role, which Cpc2p performs in controlling the G protein-mediated mating response pathway. Despite structural similarity to Gβ-like subunits, Cpc2p appears not to function at the G protein level. However, upon pheromone stimulation, cells overexpressing Cpc2p display substantial cell morphology defects, disorientation of septum formation and a significantly protracted G1 arrest. Cpc2p has the potential to function at multiple positions within the pheromone response pathway. We provide a mechanistic interpretation of this novel data by linking Cpc2p function, during the mating response, with its previous described interactions with Ran1p/Pat1p. We suggest that overexpressing Cpc2p prolongs the stimulated state of pheromone-induced cells by increasing ste11 gene expression. These data indicate that Cpc2p regulates the pheromone-induced cell cycle arrest in fission yeast by delaying cells entry into S phase

HPV16 oncogene expression levels during early cervical carcinogenesis are determined by the balance of epigenetic chromatin modifications at the integrated virus genome.

In cervical squamous cell carcinomas, high-risk human papillomavirus (HRHPV) DNA is usually integrated into host chromosomes. Multiple integration events are thought to be present within the cells of a polyclonal premalignant lesion and the features that underpin clonal selection of one particular integrant remain poorly understood. We previously used the W12 model system to generate a panel of cervical keratinocyte clones, derived from cells of a low-grade premalignant lesion naturally infected with the major HRHPV type, HPV16. The cells were isolated regardless of their selective advantage and differed only by the site of HPV16 integration into the host genome. We used this resource to test the hypothesis that levels of HPV16 E6/E7 oncogene expression in premalignant cells are regulated epigenetically. We performed a comprehensive analysis of the epigenetic landscape of the integrated HPV16 DNA in selected clones, in which levels of virus oncogene expression per DNA template varied ~6.6-fold. Across the cells examined, higher levels of virus expression per template were associated with more open chromatin at the HPV16 long control region, together with greater loading of chromatin remodelling enzymes and lower nucleosome occupancy. There were higher levels of histone post-translational modification hallmarks of transcriptionally active chromatin and lower levels of repressive hallmarks. There was greater abundance of the active/elongating form of the RNA polymerase-II enzyme (RNAPII-Ser2P), together with CDK9, the component of positive transcription elongation factor b complex responsible for Ser2 phosphorylation. The changes observed were functionally significant, as cells with higher HPV16 expression per template showed greater sensitivity to depletion and/or inhibition of histone acetyltransferases and CDK9 and less sensitivity to histone deacetylase inhibition. We conclude that virus gene expression per template following HPV16 integration is determined through multiple layers of epigenetic regulation, which are likely to contribute to selection of individual cells during cervical carcinogenesis.This work was supported by Cancer Research UK (Programme Grant A13080); the Medical Research Council; The Pathological Society of Great Britain and Ireland (E.L.A.K.); and the Agency for Science, Technology and Research, Singapore (Q.Y.A).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/onc.2016.

Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions

The control and interaction between nitrogen and carbon assimilatory pathways is essential in both photosynthetic and non-photosynthetic tissue in order to support metabolic processes without compromising growth. Physiological differences between the basal and mature region of wheat (Triticum aestivum) primary leaves confirmed that there was a change from heterotrophic to autotrophic metabolism. Fourier Transform Infrared (FT-IR) spectroscopy confirmed the suitability and phenotypic reproducibility of the leaf growth conditions. Principal Component–Discriminant Function Analysis (PC–DFA) revealed distinct clustering between base, and tip sections of the developing wheat leaf, and from plants grown in the presence or absence of nitrate. Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf. The operation of nitrogen metabolism impacted on the levels and distribution of amino acids, organic acids and carbohydrates within the wheat leaf. In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover. The value of using this combined metabolomics analysis for further functional investigations in the future are discussed