Miracle-dz/UniFAR-FTUs huggingface dataset:
Out to sea: ocean currents and patterns of asymmetric gene flow in an intertidal fish species

Passive dispersal via wind or ocean currents can drive asymmetric gene flow, which influences patterns of genetic variation and the capacity of populations to evolve in response to environmental change. The mangrove rivulus fish (Kryptolebias marmoratus), hereafter “rivulus,” is an intertidal fish species restricted to the highly fragmented New World mangrove forests of Central America, the Caribbean, the Bahamas, and Florida. Mangrove patches are biological islands with dramatic differences in both abiotic and biotic conditions compared to adjacent habitat. Over 1,000 individual rivulus across 17 populations throughout its range were genotyped at 32 highly polymorphic microsatellites. Range-wide population genetic structure was evaluated with five complementary approaches that found eight distinct population clusters. However, an analysis of molecular variance indicated significant population genetic structure among regions, populations within regions, sampling locations within populations, and individuals within sampling locations, indicating that rivulus has both broad- and fine-scale genetic differentiation. Integrating range-wide genetic data with biophysical modeling based on 10 years of ocean current data showed that ocean currents and the distance between populations over water drive gene flow patterns on broad scales. Directional migration estimates suggested some significant asymmetries in gene flow that also were mediated by ocean currents and distance. Specifically, populations in the center of the range (Florida Keys) were identified as sinks that received migrants (and alleles) from other populations but failed to export individuals. These populations thus harbor genetic variation, perhaps even from extirpated populations across the range, but ocean currents and complex arrangements of landmasses might prevent the distribution of that genetic variation elsewhere. Hence, the inherent asymmetry of ocean currents shown to impact both genetic differentiation and directional migration rates may be responsible for the complex distribution of genetic variation across the range and observed patterns of metapopulation structure.
Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity

Classes of set functions along with a choice of ground set are a bedrock to determine corresponding variants of greedy algorithms. These algorithms in turn obtain approximate and efficient solutions for combinatorial optimization of these set functions. The class of constrained submodular optimization has seen huge advances at the intersection of good computational efficiency, versatility and approximation guarantees while unconstrained submodular optimization is NP-hard. What is an alternative to situations when submodularity does not hold? Can efficient and globally exact solutions be obtained? We introduce one such new frontier: The class of quasi-concave set functions induced as a dual class to monotone linkage functions. We provide a parallel algorithm with a time complexity over $n$ processors of $O(n^{2}g)+O$ (log log $n$) where $n$ is the cardinality of the ground set and $g$ is the complexity to compute the monotone linkage function that induces a corresponding quasi-concave set function via a duality. The complexity reduces to $O(gn\log(n))$ on $n^{2}$ processors and to $O{(gn)}$ on $n^{3}$ processors. Our approach reduces the currently existing cubic computational complexity to those mentioned above. Our algorithm provides a globally optimal solution to a maxi-min problem as opposed to submodular optimization which is approximate. We show a potential for widespread applications via an example of diverse feature subset selection with exact global maxi-min guarantees upon showing that a statistical dependency measure called distance correlation can be used to induce a quasi-concave set function.
Trastuzumab-Modified Gold Nanoparticles Labeled with 211At as a Prospective Tool for Local Treatment of HER2-Positive Breast Cancer

Highly localized radiotherapy with radionuclides is a commonly used treatment modality for patients with unresectable solid tumors. Herein, we propose a novel α-nanobrachytherapy approach for selective therapy of human epidermal growth factor receptor 2 (HER2)-positive breast cancer. This uses local intratumoral injection of 5-nm-diameter gold nanoparticles (AuNPs) labeled with an α-emitter (211At), modified with polyethylene glycol (PEG) chains and attached to HER2-specific monoclonal antibody (trastuzumab). The size, shape, morphology, and zeta potential of the 5 nm synthesized AuNPs were characterized by TEM (Transmission Electron Microscopy) and DLS (Dynamic Light Scattering) techniques. The gold nanoparticle surface was modified by PEG and subsequently used for antibody immobilization. Utilizing the high affinity of gold for heavy halogens, the bioconjugate was labelled with 211At obtained by α irradiation of the bismuth target. The labeling yield of 211At was greater than 99%. 211At bioconjugates were stable in human serum. Additionally, in vitro biological studies indicated that 211At-AuNP-PEG-trastuzumab exhibited higher affinity and cytotoxicity towards the HER2-overexpressing human ovarian SKOV-3 cell line than unmodified nanoparticles. Confocal and dark field microscopy studies revealed that 211At-AuNP-PEG-trastuzumab was effectively internalized and deposited near the nucleus. These findings show promising potential for the 211At-AuNP-PEG-trastuzumab radiobioconjugate as a perspective therapeutic agent in the treatment of unresectable solid cancers expressing HER2 receptors.
Climate-related declines in reproductive output in a high-elevation population of ground squirrels

Beginning in the 1900s, changes in climate have resulted in an increase in global temperatures. This warming has driven behavioral and demographic changes within a multitude of species worldwide. Historically, small mammal populations have responded to changes in temperature by adjusting their geographic ranges. As temperatures rise, high-elevation populations may no longer be able to adjust as they have before. In this study, we evaluated possible effects of climate change on a high-elevation population of Belding’s ground squirrels (Urocitellus beldingi) at Tioga Pass in Northern California. We used local climate data and longterm population data from 1994 to 2021 at Tioga Pass to identify trends for change. A significant increase in mean annual daily minimum and daily mean temperature was observed. We also observed a significant decrease in the yearly number of litters weaned. Annual means of daily minimum and daily mean temperature are reliable predictors of the number of litters weaned during a year. Declines in the number of litters weaned varied across areas within the study site suggest that possible effects of climate change on the U. beldingi population may have been affected by local features within the overall habitat. Body mass of juvenile U. beldingi near the time of weaning increased, suggesting that maternal female U. beldingi may be better able to channel energy to their offspring under low-density conditions. Declines in the U. beldingi population at Tioga Pass are consistent with models predicting losses of high-elevation populations of small mammals as the climate continues to warm.
Multi-task Learning with BERT in NLP

In natural language processing, while deep learning techniques have achieved remarkable success in many different problems, these models are mostly task specific. When facing a situation in which multiple tasks are solved at the same time, multi-task learning is a popular paradigm to sufficiently utilize information across different tasks and to learn more efficiently or effectively. With the adoption of large language models like BERT in NLP, however, there is little research to enhance BERT to improve the performance on additional target tasks further. The downstream tasks we are interested in are Sentiment Analysis, Paraphrase Detection and Semantic Textual Similarity. In this project, we propose a multi-task learning framework based on BERT, then experiment extensions to improve the performance on the 3 tasks simultaneously. We have explored models with different architectures, different loss functions, different optimizers, adversarial learning and contrastive learning.
The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization Competition

We organized an online fingerprint-based indoor localization competition in 2021. It attracted 1,170 teams worldwide. The teams were provided with a 60 GB dataset including WiFi, BLE, IMU, and geomagnetic field strength data collected from 204 buildings to build their localization algorithms, which were then evaluated against a separate test dataset. The competition received 28,009 submissions. The top team achieved an average accuracy of 1.50m. This paper reports the lessons we learned from analyzing the submissions, as well as our experiences in organizing the competition, through both qualitatively studying the teams' algorithms and quantitatively characterizing the competition results.
Evaluating Open-Source Facial Recognition Software in Public Security: Effectiveness and Observations on Ethnicity

Context: In the criminal investigation environment, there is often a lack of information about a particular suspect, demanding instruments capable of searching for information based on limited evidence. Facial recognition, utilizing archived photos and/or real-time image capture, acts as one such instrument. Objective: This study aims to analyze the facial recognition results of the an open-source and free product, evaluating its effectiveness through acceptable accuracy and sensitivity rates for investigators, with the intention of exploring its potential application in the field of public security. Additionally, an analysis of efficacy by ethnicity was performed, which discussed solutions to avoid racism. Method: A controlled in vitro experiment was conducted, employing a dataset of approximately 20,000 authentic photographs of incarcerated individuals from the prison system of the state of Sergipe, Brazil. Results: The effectiveness results obtained indicate that the open-source and free Face Recognition tool holds potential for identifying individuals in the context of front-view photos of inmates in the prison system and similar Public Security Management applications. Upon completion of the tests and taking into account statistical significance, the software successfully identified incarcerated individuals using their images, achieving an average accuracy rate of over
Early Prediction of High Performance Computing Job Outcomes via Modeling System Text Logs

—The massive scale of high performance computing (HPC) machines necessitates using automatic statistical methods to assist human operators in monitoring day-to-day behavior. We address the problem of identifying problematic compute jobs by modeling system logs, which record all activities on the machine in near-natural language form. We apply techniques from relational learning and human language technology, incorporated with domain knowledge, to extract features from system logs produced by approximately 10,000 HPC jobs. We evaluate the usefulness of these features via a random forest model to predict job outcome state. We compare our models to a baseline which mimics state-of-the-art human operator behavior, and ﬁnd that the best-performing feature set is one which combines domain knowledge with simple aggregate metrics. Our method predicts job outcomes with an F1 score approaching 0.9 after a job has been running for 30 minutes, giving an average lead time of 3 hours before failure.
Multiple Strategies to Improve minBERT Multitask Learning

The Bidirectional Encoder Representations from Transformers or BERT is a transformer-based model that generates contextual word representations [1]. This project has focused on using this model to train and perform across the different tasks: sentiment classification, paraphrase classification, and sentence similarity scoring. A model that could perform this tasks well would be useful as it would not require multiple different models and a demonstration the generalizability of the BERT architecture. Improvement over the baseline implementation came from testing different multitask learning methods and pretraining on in-domain data. Cosine similarity finetuning to improve sentence similarity tasks was also tested but did not show improvement.
Pakistan's Contribution to the Global Rise in Conventional Pollution

This thesis examines the impact of conventional pollutants on the environment, communities, and individuals in Pakistan. It begins by investigating the causes of conventional pollutants and exploring Pakistan’s agricultural industry, domestic practices, non-renewable energy sources, and industrial practices (the brick industry, leather tanning industry, and textile industry). It then analyzes factors that continue to contribute to conventional pollution in Pakistan such as economic incentives, politics, and traditional practices within industries. The thesis then delves into measures that have already been taken by Pakistan and other organizations to decrease conventional pollutants and then suggests potential opportunities for improvement.
Branch-Price-and-Cut for Causal Discovery

We show how to extend the integer programming (IP) approach to score-based causal discovery by including pricing . Pricing allows the addition of new IP variables during solving, rather than requiring them all to be present initially. The dual values of acyclicity constraints allow this addition to be done in a principled way. We have extended the GOBNILP algorithm to effect a branch-price-and-cut method for DAG learning. Empirical results show that implementing a delayed pricing approach can be beneficial. The current pricing algorithm in GOBNILP is slow, so further work on fast pricing is required.
The Dynamic Effects of Cash Transfers: Evidence from Rural Liberia and Malawi∗

We study the dynamic effects of large, unconditional cash transfers in rural Liberia and Malawi using bi-monthly phone surveys. We document improvements in food security until the end of surveying (about a year in Liberia and two years in Malawi), but find a short-lived effect on food expenditures and no effect on non-agricultural income at any point. Increased food security appears to have been generated by increased productive investment. After 18-25 months, we also document improvements in IPV, psychological well-being, and resilience, as well as investment and agricultural output. We find no evidence of effects on local prices or of spillovers to untreated households. JEL classification: C93, G51, I31, O12
Shear Wave Splitting Analysis using Deep Learning (SWSNet)

Teleseismic shear-wave splitting analyses are typically performed by reversing the splitting process through the application of frequency-or time-domain operations minimizing transverse-component waveforms. These operations yield two splitting parameters, φ (fast-axis orientation) and δ t (delay time). In this study, we investigate the applicability of a recurrent neural network, SWSNet, for determining the splitting parameters from pre-selected waveform windows. Due to the scarcity of suﬃciently labelled real waveform data, we generate our own synthetic training dataset. The model is capable of determining φ and δ t with a root mean squared error (RMSE) of 9.58* and 0.143 s for noisy synthetic test data. The application to real data involves a deconvolution step to homogenize the waveforms. When applied to data from the USArray dataset, the results exhibit similar patterns to those found in previous studies with mean absolute diﬀerences of 11.12*
Modeling High Performance Computing System Log

As the high performance computing community prepares for the exascale computing era, progress in unsupervised computer maintenance and error prevention becomes increasingly important. Computer maintenance must become semi-supervised and eventually, unsupervised, due to the large amount of information to monitor from the machines, such as system logs, job logs, and temperature reports. A human analyst cannot keep up with this large information flow. Since unassisted human monitoring is not efficient, we turn to other, semi-supervised machine learning methods. Our work uses machine learning techniques to investigate correlations between a computer’s system logs and job logs, specifically whether system log behavior can predict the outcome of a job. Outcome refers to the successful completion of a job.Wematch job log entries with corresponding system log entries and evaluate the usefulness of various system log features for outcome prediction. With these features, we create a job prediction model and test its effectiveness on predicting the outcome of a job at various points in the job’s run time, such as number of messages into the job and time passed since the job’s start.
Acute Liver Failure Guidelines

Acute liver failure (ALF) is a rare, acute, potentially reversible condition resulting in severe liver impairment and rapid clinical deterioration in patients without preexisting liver disease. Due to the rarity of this condition, published studies are limited by the use of retrospective or prospective cohorts and lack of randomized controlled trials. Current guidelines represent the suggested approach to the identification, treatment, and management of ALF and represent the official practice recommendations of the American College of Gastroenterology. The scientific evidence was reviewed using the Grading of Recommendations, Assessment, Development and Evaluation process to develop recommendations. When no robust evidence was available, expert opinions were summarized using Key Concepts. Considering the variety of clinical presentations of ALF, individualization of care should be applied in specific clinical scenarios.
Nonlinear Fixed Points and Stationarity: Economic Applications∗

We consider the fixed points of nonlinear operators that naturally arise in games and general equilibrium models with endogenous networks, dynamic stochastic games, and in models of opinion dynamics with stubborn agents. We study limit cases that correspond to high coordination motives, infinite patience, and vanishing stubbornness in the applications above. Under monotonicity and continuity assumptions, we provide explicit expressions for the limit fixed points. We show that, under differentiability, the limit fixed point is linear in the initial conditions and characterized by the Jacobian of the operator at any constant vector with an explicit and linear rate of convergence. Without differentiability, but under additional concavity properties, the multiplicity of Jacobians is resolved by a representation of the limit fixed point as a maxmin functional evaluated at the initial conditions. In our applications, we use these results to characterize the limit equilibrium actions, prices, and endogenous networks, show the existence of the asymptotic value in a class of zero-sum stochastic games with a continuum of actions, and compute a nonlinear version of the eigenvector centrality of agents in networks. ∗We wish to thank Oguzhan Celebi, Joel Flynn, Drew Fudenberg, Ben Golub, Matt Jackson, Peter Klibanoff, Nicolas Lambert, Stephen Morris, Simeon Reich, Karthik Sastry, Lorenzo Stanca, Omer Tamuz, Nicolas Vieille, and Alex Wolitzky for useful comments. Roberto Corrao gratefully acknowledges the financial support of the Gordon Pye fellowship.
Enhancing minBert Embeddings for Multiple Downstream Tasks

Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based model that generates contextual word embed-dings. Starting with a minimal implementation of BERT, called minbert, I implemented Multi-head Self-Attention and the Transformer Layer, including a portion of the Adam stochastic optimization method. My goal was to make enhancements to obtain robust and generalizable embeddings that perform well in multiple downstream tasks: sentiment analysis, paraphrase detection and semantic textual similarity. The completed base implementation achieved a 38.5% accuracy, 37.5% accuracy and -0.074 correlation respectively (overall average of 0.229) on holdout datasets of the aforementioned tasks. After further pre-training on task specific data by training on a masked language model objective, fine-tuning using cosine embedding loss, applying a learning rate decay schedule, and hyperparameter tuning, my final model achieved a 52.6% accuracy, 59.3% accuracy and 0.418 correlation respectively (overall average of 0.512) on the same datasets, a 124% increase over the base implementation.
CAMBRIDGE WORKING PAPERS IN ECONOMICS Optimal Feasible Expectations in Economics and Finance

Trying to estimate rational expectations does not usually minimise forecast error when forecasting macroeconomic or financial variables in reality. This is because, with samples of realistic length, optimal feasible forecasts contain conditional biases that reduce forecast variance. I demonstrate this by using penalised factor models to show that statistically simple inflation forecasts, primarily based on past inflation, are optimal even when other relevant financial and economic variables are available. I also show that US household inflation forecasts display many similarities to these simple optimal forecasts, but also contain mistakes that increase forecast error. Therefore a combination of `optimal feasible expectations' and behavioural errors explain US household inflation forecasts. This suggests that optimal feasible expectations, with additional behavioural errors in some cases, could explain forecast formation across economics and finance. Reference Details CWPE 20105 Published 11 November 2020
Monitoring the abundance of microplastics in mussels and marine ecosystems may indicate human health risks

The numerous scientific methodologies and procedures used in marine research on microplastic pollution restrict our present understanding of this severe environmental issue threatening marine organisms and human health. The abundance and bioaccumulation of microplastic in the food web is causing adverse implications for marine wildlife and potentially human health via seafood consumption. The absence of standardized methods for quantifying and characterizing microplastics is a major limiting factor in comparing current research. To evaluate the issue of monitoring microplastics in the marine environment, I conducted a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis on mussels as a biomonitoring tool. Because mussels are resilient, abundant, accessible worldwide, and commonly used as bioindicators in the marine environment, they retain higher microplastic particles and are significant biomarkers for microplastic particles. Furthermore, I conducted a comparative analysis of five different trash capture systems for fighting microplastic pollution. Among the five trash capturing methods, end of pipe nets and bioretention rain gardens were cost-effective and efficient in the long run. I suggested the implementation of a Total Maximum Daily Loads for trash (TMDLs) and the trash capture system to aid in a framework for achieving the zero waste goals by 2030 in California. Recommendations for microplastic management include a two-track strategy: (1) implementing preventative measures, clean-up pathways, and regulatory initiatives; (2) advancing scientific research through risk assessments and thresholds to improve existing solutions by adopting the standardized protocol.