"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction

Social media has become a major driver of social change, by facilitating the formation of online social movements. Automatically understanding the perspectives driving the movement and the voices opposing it, is a challenging task as annotated data is difficult to obtain. We propose a weakly supervised graph-based approach that explicitly models perspectives in #BackLivesMatter-related tweets. Our proposed approach utilizes a social-linguistic representation of the data. We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives. Our approach uses a small seed set of labeled examples. We experiment with large language models for generating artificial training examples, compare them to manual annotation, and find that it achieves comparable performance. We perform quantitative and qualitative analyses using a human-annotated test set. Our model outperforms multitask baselines by a large margin, successfully characterizing the perspectives supporting and opposing #BLM.Comment: Findings of EMNLP 2023 (Camera Ready

Large mammalian herbivores and the paradox of soil carbon in grazing ecosystems: Role of microbial decomposers and their enzymes

Grazing is the dominant land use across the world, and large mammalian herbivores exert strong influence over biogeochemical cycles. Grazing ecosystems feature C-rich soils, even though herbivores consume a major fraction of plant production to reduce detrital input to soil. Yet, counter-intuitively, moderate grazing can promote net soil-C storage in many ecosystems compared to grazer-exclusion. We address this enigmatic influence of grazers on soil-C and test their indirect effect on proximate drivers of decomposition: microbial extracellular enzyme activity. We used a replicated long-term grazer-exclusion experiment to measure responses in above- and belowground plant biomass, soil-C stock, microbial biomass, labile/recalcitrant C pools and three enzymes relevant to the C-cycle: peroxidase—which initiates decomposition of recalcitrant matter, alongside beta-glucosidase and cellobiohydrolase—which act further downstream on more labile fractions. Consistent with other ecosystems, upto 12 years of herbivore exclusion did not increase soil-C in the fenced plots despite higher plant biomass and higher potential detrital C-inputs. Grazer-exclusion did not alter microbial biomass; peroxidase increased threefold and beta-glucosidase was doubled; cellobiohydrolase was unaffected. Grazer-exclusion also led to twofold increase in recalcitrant-C and in microbial respiration, but it did not influence labile-C. Structural equation models supported the hypothesis that grazing favours soil-C via its indirect effect on peroxidase, but they did not support that the effects can run in the opposite direction where soil-C affects enzymes. Grazer-mediated shifts in how microbes deploy enzymes emerge as a plausible mechanism that affects soil-C. These linkages may be important to maintain soil-C sequestration in drylands which support large mammalian herbivores

Conversation Style Transfer using Few-Shot Learning

Conventional text style transfer approaches for natural language focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer on conversations such as task-oriented dialogues, existing approaches suffer from these limitations as context can play an important role and the style attributes are often difficult to define in conversations. In this paper, we introduce conversation style transfer as a few-shot learning problem, where the model learns to perform style transfer by observing only the target-style dialogue examples. We propose a novel in-context learning approach to solve the task with style-free dialogues as a pivot. Human evaluation shows that by incorporating multi-turn context, the model is able to match the target style while having better appropriateness and semantic correctness compared to utterance-level style transfer. Additionally, we show that conversation style transfer can also benefit downstream tasks. Results on multi-domain intent classification tasks show improvement in F1 scores after transferring the style of training data to match the style of test data

Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool

Grazing by mammalian herbivores can be a climate mitigation strategy as it influences the size and stability of a large soil carbon (soil-C) pool (more than 500 Pg C in the world’s grasslands, steppes, and savannas). With continuing declines in the numbers of large mammalian herbivores, the resultant loss in grazer functions can be consequential for this soil-C pool and ultimately for the global carbon cycle. While herbivore effects on the size of the soil-C pool and the conditions under which they lead to gain or loss in soil-C are becoming increasingly clear, their effect on the equally important aspect of stability of soil-C remains unknown. We used a replicated long-term field experiment in the Trans-Himalayan grazing ecosystem to evaluate the consequences of herbivore exclusion on interannual fluctuations in soil-C (2006 to 2021). Interannual fluctuations in soil-C and soil-N were 30 to 40% higher after herbivore exclusion than under grazing. Structural equation modeling suggested that grazing appears to mediate the stabilizing versus destabilizing influences of nitrogen (N) on soil-C. This may explain why N addition stimulates soil-C loss in the absence of herbivores around the world. Herbivore loss, and the consequent decline in grazer functions, can therefore undermine the stability of soil-C. Soil-C is not inert but a very dynamic pool. It can provide nature-based climate solutions by conserving and restoring a functional role of large mammalian herbivores that extends to the stoichiometric coupling between soil-C and soil-N

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV