Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven exploration of the variability, controls and future changes of dimethyl sulfide in the global surface ocean

As the largest natural source of sulfur-containing gases into the atmosphere, ocean organism-derived dimethyl sulfide (DMS) has been considered to play a critical role in the Earth’s climate system. Yet there are great uncertainties in modeling the spatiotemporal variations of DMS and incomplete knowledge of influencing factors in different oceanic regions. Moreover, little is known about the future change of global DMS, which limits our understanding of the feedback of marine ecosystem to climate change. Here we develop an artificial neural network model and combine data mining approaches to address these issues. Phytoplankton biomass and salinity are currently predominant factors associated with DMS variability in the coastal and Arctic regions, respectively. In the midand low-latitude open oceans, nutrients and temperature are also crucial factors in addition to radiation and mixed layer depth, and their relationships with DMS show reversals when passing certain thresholds. Although the global average DMS concentration and emission slightly decline from 2005 to 2100, they may change considerably in specific regions. In contrast to the DMS decreases in the low-latitudes mainly related with phosphate reduction and temperature rise and in the North Atlantic subpolar gyre attributed to salinity decline, warming will cause DMS increase in the Southern Ocean and sea ice loss will dramatically enhance DMS emission in the Arctic. Although the global negative feedback loop between oceanic DMS and climate may not operate, the future spatial redistribution of DMS may lead to the change in cloud cover pattern and significantly affect regional climate.
Loaded Language and Conspiracy Theorizing

Loaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, we examine multiple types of loaded language in two online parenting forums: one historically against vaccination, and another historically accepting of vaccination. It is well-established that conspiracy theories are the most influential contributor to anti-vaccination views (Hornsey et al., 2018) and anti-vaccination beliefs are strongly correlated with belief in unrelated conspiracy theories (Goldberg & Richey, 2020). Results indicate that users of an anti-vaccination forum use a greater frequency of loaded language to express themselves than users of a vaccinationneutral forum.
Trusting the Untrustworthy: A Cautionary Tale on the Pitfalls of Training-based Rejection Option

We consider the problem of selective classification, also known as rejection option. We first analyze state-of-the-art methods that involve a training phase to produce a selective classifier capable of determining when it should abstain from making a decision. Although only some of these frameworks require changes to the basic architecture of the classifier, by adding a module for selection, all methods necessitate implementing modifications to the standard training procedure and loss function for classification. Crucially, we observe two types of limitations affecting these methods: on the one side, these methods exhibit poor performance in terms of selective risk and coverage over some classes, which are not necessarily the hardest to classify; and surprisingly, on the other side, the classes for which they attain low performance vary with the model initialization. Additionally, some of these methods also decrease the accuracy of the final classification. We discuss the limitations of each framework, demonstrating that these shortcomings occur for a wide range of models and datasets. Moreover, we establish a formal connection between the trade-off of detecting misclassification errors and the minimization of risks in selective classification. This connection enables the development of a testing framework that requires no training and can be seamlessly applied to any pre-trained classifier, thereby enabling a rejection option.
GrayC: Greybox Fuzzing of Compilers and Analysers for C

Fuzzing of compilers and code analysers has led to a large number of bugs being found and fixed in widely-used frameworks such as LLVM, GCC and Frama-C. Most such fuzzing techniques have taken a blackbox approach, with compilers and code analysers starting to become relatively immune to such fuzzers. We propose a coverage-directed, mutation-based approach for fuzzing C compilers and code analysers, inspired by the success of this type of greybox fuzzing in other application domains. The main challenge of applying mutation-based fuzzing in this context is that naive mutations are likely to generate programs that do not compile. Such programs are not useful for finding deep bugs that affect optimisation, analysis, and code generation routines. We have designed a novel greybox fuzzer for C compilers and analysers by developing a new set of mutations to target common C constructs, and transforming fuzzed programs so that they produce meaningful output, allowing differential testing to be used as a test oracle, and paving the way for fuzzer-generated programs to be integrated into compiler and code analyser regression test suites. We have implemented our approach in GrayC, a new open-source LibFuzzer-based tool, and present experiments showing that it provides more coverage on the middle- and back-end stages of compilers and analysers compared to other mutation-based approaches, including Clang-Fuzzer, PolyGlot, and a technique similar to LangFuzz. We have used GrayC to identify 30 confirmed compiler and code analyser bugs: 25 previously unknown bugs (with 22 of them already fixed in response to our reports) and 5 confirmed bugs reported independently shortly before we found them. A further 3 bug reports are under investigation. Apart from the results above, we have contributed 24 simplified versions of coverage-enhancing test cases produced by GrayC to the Clang/LLVM test suite, targeting 78 previously uncovered functions in the LLVM codebase.
Monitoring with Small Stakes

This paper proposes a mechanism that addresses the “monitoring with small stakes” puzzle in syndicated lending. We introduce a framework identifying two incentive sources for creditors: skin-in-the-game and rent extraction from renegotiation. Our analysis suggests that rent extraction is crucial in providing monitoring incentives and facilitating institutional investors’ participation in syndicated lending. We use the passage of a tax policy that exogenously reduced renegotiation frictions to identify the key channel of our theoretical framework. Our findings indicate that in deals with lower bank skin-in-the-game, a less frictional renegotiation environment is associated with greater rent extraction, monitoring, and improved firm performance.
Multitask minBert

BERT is a state-of-the-art, pretrained language representation model that can be fine-tuned to perform well on a variety of natural language processing tasks (Devlin et al., 2018). Our goal is to build a multitask BERT model that can simultaneously perform well on three sentence-level tasks: sentiment classification, paraphrase detection, and semantic textual similarity. The three main techniques we explore are further pretraining, single-task fine-tuning, and multitask fine-tuning. We find that (1) only multitask fine-tuning outperforms only single-task fine-tuning, (2) additional pretraining does not significantly improve multitask model performance, (3) adding dense task-specific layers improves model performance, and (4) single-task fine-tuning after multitask fine-tuning improves model performance on tasks with comparatively low training performance.
On The Effectiveness of Gender Debiasing Methods in Removing Gender Information From Model Representations

Large pre-trained models such as BERT have 001 been shown to demonstrate biased behavior 002 towards different demographic groups, such 003 as gender, race, or religion. Despite the de-004 velopment and proposal of various debiasing 005 methods, there is a paucity of prior research 006 focusing on the efficacy of debiasing methods 007 in removing the latent demographic informa-008 tion encoded in internal representations. We 009 examine the effectiveness of some recent bias 010 mitigation methods in removing stereotypical 011 gender information from internal model repre-012 sentations using Minimum Description Length 013 (MDL) probing. We discover that the effective-014 ness of current debiasing techniques might not 015 necessarily be indicative of reduced latent gen-016 der bias in representations. Furthermore, we in-017 vestigate the effect of debiasing methods on in-018 ternal representations using layerwise probing, 019 showing that they tend to concentrate gender 020 information in a few layers. We additionally 021 apply a number of state-of-the-art debiasing 022 methods to the layers with the highest gender 023 information concentration, finding that by fo-024 cusing on these layers, there is only a minimal 025 change in model behavior with respect to fair-026 ness and performance. 027
CAMBRIDGE WORKING PAPERS IN ECONOMICS JANEWAY INSTITUTE WORKING PAPERS The Impact of Fear of Automation

In this paper, we establish a causal effect of workers’ perceived probability of losing one’s job due to automation on preferences for redistribution and intentions to join a union. In a representative sample of the US workforce, we elicit the perceived fear of losing one’s job to robots or artificial intelligence. We document a strong relationship between fear of automation and intentions to join a union, retrain and switch occupations, preferences for higher taxation, higher government handouts, populist attitudes, and voting intentions. We then show a causal effect of providing information about job loss probabilities on preferred levels of taxation and handouts. In contrast, our information treatment does not affect workers’ intentions to self-insure by retraining or switching occupations, but it increases workers’ self-reported likelihood of joining a union to seek more job protection. The treatment effects are mostly driven by workers who are informed about larger job loss probabilities than they perceived. Reference Details 2269 Cambridge Working Papers in Economics 2229 Janeway Institute Working Paper Series Published 1 December 2022
Trends in Health Outcomes Before and After Long Term Care Federal Regulations Limited Antipsychotic Medication Use: A Secondary Data Analysis

2 Chapter One . 5 Specific Aims . 5 Significance.6 High Reliability Theory . 11 Importance of Research to Nursing . 12 Innovation . 13 Approach . 14 Methods.15 Limitations . 18 Chapter Two.21 Background . 23 Study Purpose . 25 Method . 25 Data Analysis . 27 Results . 27 Discussion . 29 Limitations . 32 Implications for Future Research . 34 Conclusion . 36 References . 37
Bert-Powered Book Genre Classification

The vast number of e-books available presents a challenge in automatically labeling and organizing them. This paper addresses this issue by proposing a genre classification model that uses only book titles, a readily available and informative input, to predict one of 32 genres. The main contributions of this paper include extensive experiments on various approaches to finetune a Bidirectional Encoder Representations from Transformers (BERT) model for book title genre classification, including pretraining on a Masked Language Modeling objective, performing Supervised Contrastive Learning, and using the Sharpness-Aware Minimization optimizer. The final model achieved 71.2% accuracy, outperforming previous models and baseline architectures. However, a limitation of our model is that it assumes each book belongs to a single genre and does not account for the possibility of books fitting into multiple genres.
Double trouble: Predicting new variant counts across two heterogeneous populations

Collecting genomics data across multiple heterogeneous populations has the potential to improve our understanding of disease. For instance, we might compare cases and controls to discover biological underpinnings of disease and ultimately develop treatments. Or we might compare multiple cancer types to understand etiology and develop more broadly-applicable therapies. Despite sequencing advances, though, resources remain a constraint in how much data can be gathered. If we could predict the amount of new information to be gained from different potential amounts of data, that could guide experimental design to make optimal use of the resources at hand. And while many authors have developed prediction methods for the single-population case [Masoero et al., 2022, Chakraborty et al., 2019, Zou et al., 2016, Gravel and National Heart Lung & Blood Institute (NHLBI) GO Exome Sequencing Project, 2014, Ionita-Laza et al., 2009], that work will generally provide poor-quality predictions across multiple heterogeneous populations when these populations can share information. We develop a new methodology to provide accurate predictions in the face of multiple populations.
Shortening the Path to Productive Investment: Evidence from Input Fairs and Cash Transfers in Malawi

While cash transfers consistently show large effects on immediate outcomes like consumption, limited access to markets may mute their impact on productive investment. In an experiment in Malawi, we cross-cut cash transfers with an “input fair”, designed to reduce transport costs to access agricultural inputs. Cash alone increases investment by 25%, while the input fair doubles this effect. Input fairs alone were ineffective. A mistimed public subsidy program undoes the incremental effect of the joint intervention (though not of cash alone) by causing input fair purchases to crowd out subsidized inputs, such that the program had no effect on quantities. JEL Codes: O13, Q12
CAMBRIDGE WORKING PAPERS IN ECONOMICS Health Insurance and Agricultural Investments: Evidence from Rural Thailand

Exploiting the 2001 universal health insurance reform in Thailand as a source of identification, we estimate the effects of health insurance coverage on agricultural production decisions and welfare. Our estimates suggest that the reform led to long-run increases in total cultivation investments and output, and that households shifted their cultivation portfolio towards riskier crops. We explain these findings using a model of agricultural investment, highlighting the important roles of health insurance in terms of mitigating background medical expenditure risk and improving health. We also find that the reform improved households’ welfare by reducing debts and defaults on loans. Reference Details CWPE 2327 Published 20 March 2023
Aridification of Northwest Australia and Nutrient Decline in the Timor Sea During the 40 Kyr World

Studying tropical hydroclimate and productivity change in the past is critical for understanding global climate dynamics. Northwest Australia is an ideal location for investigating Australian monsoon dynamics, the variability of the Indonesian Throughflow (ITF), and their impact on past productivity and Pacific warm pool evolution, which remain poorly understood during the 40 kyr world in the mid‐early Pleistocene. In this study, we present multi‐proxy records from International Ocean Discovery Program (IODP) Site U1483 in the Timor Sea spanning the last 2,000 ka, including orbitally‐resolved records from the 40 kyr world between 2,000 and 1,300 ka. Our results suggest that northwest Australia underwent a step of increased aridification and that productivity in the Timor Sea declined during the transition from ∼1,700 to ∼1,400 ka. We attribute this aridification to the reduced moisture supply to this region caused by the ITF restriction and warm pool contraction. We ascribe the declined productivity to a decrease in the nutrient supply of the Pacific source water associated with global nutrient redistribution. At orbital timescale, multiple mechanisms, including sea level changes, monsoon, and the Intertropical Convergence Zone (ITCZ) dynamics, and variations in the ITF and Walker circulation could have controlled variations of productivity and terrigenous input in the Timor Sea during the 40 kyr world. Our bulk nitrogen and benthic carbon isotope records suggest a strong coupling to biogeochemical changes in the Pacific during this period. This research contributes to a better understanding of tropical hydroclimate and productivity changes during the 40 kyr world.
TikTok Made Me Buy it: Emotional Carryover of Doomscrolling on Purchasing Decisions

Doomscrolling,” defined as excessively poring through negative content on social media, has become increasingly prevalent over the past several years. Although recent research suggests that doomscrolling reduces positive emotions, even over relatively short exposure (< 5 min) times, the effect of doomscrolling on other behaviors remains unexplored. This question is especially relevant because exposure to sad movie clips has previously been shown to increase willingness-to-pay (WTP) for unrelated purchases, due to the desire to change one’s circumstances by seeking out higher rewards. In this experiment, we examined the effect of doomscrolling on purchasing decisions via exposure to video content from a popular social media platform (TikTok). Participants (N = 91) viewed 8-10 minutes of TikTok videos before choosing whether to purchase an unrelated consumer product (Apple AirPods). Participants were randomly assigned to view either content to induce sadness (thematically related to climate change) or neutral content (thematically related to nature appreciation). Relative to participants in the Nature Appreciation condition, participants in the Climate Change condition reported significantly higher levels of sadness, confirming that the emotion induction was successful. In addition, participants in the Climate Change condition had higher WTP for the AirPods. However, the results from the WTP analysis were not statistically significant, perhaps reflecting limited power due to the small sample size of this study. While methodological issues constrain the interpretation of these results, the successful replication of these findings could have practical implications for how consumers and technology companies structure decisions following extended exposure to negative content on social media.
An Experimental Study of Prisoners’ Dilemma and Stag Hunt Games Played by Teams of Players

We compare the behavior in six variations of prisoners’ dilemma and stag hunt games played by 5-member teams to behavior in the same games played by individuals. We also compare three different team collective choice procedures: majority rule; majority rule preceded by a poll; and majority rule preceded by chat. In all six games, we observe significant bandwagon effects that drive consensus in the poll and chat treatments, but voting procedures have no significant effects on team decision frequencies except for the poll treatment of the stag hunt games. Surprisingly, teams do not always defect more than individuals. In prisoners’ games with higher incentives to cooperate, teams cooperate more than individuals. Teams coordinate more successfully than individuals in all stag hunt games. Additional data are gathered using Amazon Mechanical Turk, including data for much larger (25-member) teams. The observed MTurk behavior is significantly different from laboratory behavior. JEL Classification Numbers: C72
Implementing minBERT and Extensions for Multi-Task Learning

This project studies how well a pretrained Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) model generalizes to different downstream tasks, and explores improvements to enable better generalization. BERT is known as a pretrained model that when finetuned on specific tasks with just one additional output layer, achieves outstanding results. Its power to generalize to multiple tasks all at once is not studied in the original paper and is the primary focus of our project. We introduce custom output layers and loss functions for each task, and then merge the gradient updates into a single model. Our results from the ablation study show how well these techniques work on multi-task learning
Synthesizing Guide Programs for Sound, Effective Deep Amortized Inference

In probabilistic programming languages (PPLs), a critical step in optimization-based inference methods is constructing, for a given model program, a trainable guide program. Soundness and effectiveness of inference rely on constructing good guides, but the expressive power of a universal PPL poses challenges. This paper introduces an approach to automatically generating guides for deep amortized inference in a universal PPL. Guides are generated using a type-directed translation per a novel behavioral type system. Guide generation extracts and exploits independence structures using a syntactic approach to conditional independence, with a semantic account left to further work. Despite the control-flow expressiveness allowed by the universal PPL, generated guides are guaranteed to satisfy a critical soundness condition and, moreover, consistently improve training and inference over state-of-the-art baselines for a suite of benchmarks. This technical report accompanies Li et al. [2023].
Behavior in triadic conversations in conditions with varying positions of noise distractors

Introduction Face-to-face communication can be challenging in noisy environments for everyone [1] and people often experience difficulties with speech communication in cocktail party situations. Despite identifying factors that influence spoken communication, such as the level and position of background noise sources and talkers, and the exact position of the listener's head, the precise reasons for listening problems are often unknown. Therefore, replication of the problems in laboratory is difficult which closely relates to the fact that the acoustics at the ear of people who communicate in cocktail party situations change rapidly due to the movement of participants and sound sources, and reverberation [2]–[4]. To provide a broader perspective on communication in realistic situations, the present study aims to investigate the movement behavior of people engaged in triadic conversations. We accomplish this by creating a precisely controlled audiovisual virtual reality using the real-time Simulated Open Field Environment (rtSOFE) [5].