Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning for Language Models

Recent interest in Large Language Models (LLMs) and human alignment calls for effective finetuning methods. In this work, we investigate how reinforcement learning (RL) can be used to finetune the downstream performance of pretrained Language Models (LMs). Recently, on-policy RL algorithms have shown promise for text generation tasks. However, they face several empirical challenges, including (1) training instability due to the large action space and (2) sample inefficiency. In this paper, we explore methods to address both of these limitations. First, we implemented a variety of sampling techniques which effectively restrict the total action space without compromising performance, and which show significant improvement over vanilla proximal policy optimization (PPO). Second, we implemented an off-policy and value-based algorithm: Deep-Q Learning (DQN). We demonstrate the DQN can be applied to finetuning langauge models for downstream applications. However, futher exploration and tuning is required to determine whether it can achieve better sample efficiency compared to on-policy algorithms.
Building Robust Adaptation for Multi-Task Learning over minBERT

Our motivation is to address the limitation of Projected Attention Layers (PALs). Our goal is to improve the performance of PALs with minBERT as the baseline model by experimenting with training methods, adding regularization and hyperpa-rameter optimization. During on our experiments, we saw an increase in accuracy and correlation with both PALs and low rank layers. We found that using weight decay and annealed sampling could ameliorate overfitting induced by increased model complexity, but more approaches could be explored to further improve model performance
A Mid-Range Future Climate Scenario: A Regime Shift in Northern

Climate models project a future weakening of the Atlantic Meridional Overturning Circulation (AMOC), but the impacts of this weakening on climate remain highly uncertain. A key challenge in quantifying the impact of an AMOC decline is in isolating its influence on climate, relative to other changes associated with increased greenhouse gases. Here we isolate the climate impacts of a weakened AMOC in the broader context of a warming climate using a unique ensemble of Shared Socioeconomic Pathway (SSP) 2-4.5 integrations that was performed using the Climate Model Intercomparison Project Phase 6 (CMIP6) version of the NASA Goddard Institute for Space Studies ModelE (E2.1). In these runs internal variability alone results in a spontaneous bifurcation of the ocean flow, wherein two out of ten ensemble members exhibit an entire AMOC collapse, while the other eight recover at various stages despite identical forcing of each ensemble member and with no externally prescribed freshwater perturbation. We show that an AMOC collapse results in an abrupt northward shift and strengthening of the Northern Hemisphere (NH) HC and intensification of the northern midlatitude eddy-driven jet. We then use a set of coupled atmosphere-ocean abrupt CO2 experiments spanning the range 1-5xCO2 to show that this response to an AMOC collapse results in a nonlinear shift in the NH circulation moving from 2xCO2 to 3xCO2. Slab-ocean versions of these experiments, by comparison, do not capture this nonlinear behavior. Our results suggest that changes in ocean heat flux convergences associated with an AMOC collapse — while highly uncertain — can result in profound changes in the NH circulation and continued efforts to constrain the AMOC response to future climate change are needed. 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Pushing the Limits of Fairness in Algorithmic Decision-Making

Designing provably fair decision-making algorithms is a task of growing interest and importance. In this article, I argue that preference-based notions of fairness proposed decades ago in the economics literature and subsequently explored in-depth within computer science (specifically, within the field of computational social choice) are aptly suited for a wide range of modern decision-making systems, from conference peer review to recommender systems to participatory budgeting.
The housing channel of intergenerational wealth persistence∗

We use Norwegian tax data and a life-cycle model with housing to study how wealth transmits from one generation to the next through the housing market. After controlling for a rich set of attributes, households with richer parents are one percentage point (21%) more likely to enter the housing market in a given year and buy homes worth $41,000 (20%) more upon entry. Using international stock market returns as a shift-share instrument supports a causal interpretation of parental wealth on offspring’s housing outcomes. We further document that housing outcomes when young are important determinants of midlife wealth, using plausibly exogenous variation caused by the timing of intra-family deaths. Housing gaps caused purely by parental wealth explain more than 10% of intergenerational wealth persistence. With regard to mechanisms, we find that parents use transfers, home equity withdrawals, co-purchasing and intra-family house sales at discounted prices to support their offspring. Through the lens of our model, house price expectations stand out as a key driver of the housing channel of intergenerational wealth persistence.
A cross-sectional comparison of perceived stress and emotional affect between adults with and without emotional disturbances

Perceived stress has increased due to the COVID-19 pandemic. In addition, those with diagnosed emotional disturbances note higher perceived stress ratings than those without these conditions. Emotion-focused coping can be a practical adjunctive component with problem-based solutions during elevated stress. However, it is unknown if there are differences in perceived stress levels and emotional affect in adults with and without emotional disturbances using a novel emotion-focused coping strategy. A cross-sectional study of 97 adults using the novel strategy completed an online survey of the Perceived Stress Scale (PSS-10) and Positive and Negative Affect Schedule (PANAS). Group one (NEDG) included 34 individuals without a history of emotional disturbance. Group two (EDG) included 63 individuals reporting an emotional disturbance (Anxiety; N= 58 & Depression; N=5). There was no significant difference in perceived stress between the NEDG (17.6 + 5.3) and EDG (17.0 + 5.5) groups. Moreover, there were no significant differences in PANAS between groups. Positive affect was in the 51st percentile (95 % CI: 45.7-56.6%), and negative affect was in the 74th percentile (95% CI: 70.4-78.9%) of normative data. Individuals with or without emotional disturbance may experience similar perceived stress levels and emotional affect while using a novel coping strategy. Additional research investigating this novel strategy is warranted.
The Impact of Digital Credit in Developing Economies: A Review of Recent Evidence

In recent years, a new generation of “digital credit” products have transformed the consumer lending landscape in many lowand middle-income countries. Offering shortterm, high-interest loans via mobile phones or other digital platforms, these products have become wildly popular. This article reviews the small but emerging evidence on the welfare impacts of digital credit. These studies document very high rates of takeup well in excess of traditional microcredit despite the fact that customers often do not understand the terms of their loans. Overall, there is little evidence that access to credit has consistent positive impacts on borrower welfare, though two impact evaluations document positive effects on resilience and subjective well-being, respectively. No study finds statistically significant negative impacts of digital credit. ∗Blumenstock: University of California, Berkeley and CEGA, email: jblumenstock@berkeley.edu; Park: University of California, Santa Cruz, email: davidspark@ucsc.edu; Robinson: University of California, Santa Cruz, CEGA and NBER, email: jmrtwo@ucsc.edu. We thank Natasha Beale, Leah Bridle, Dan Cassara, Marisa McKasson and Alexandra Wall for their tireless work on behalf of the DCO, and Carson Christiano for her support throughout the project. We thank Seth Garz and Rebecca Mann for their support, and additionally thank Seth for comments on this draft. Much of the research discussed in this paper was made possible (in whole or in part) by the Digital Credit Observatory (DCO), a program of the Center for Effective Global Action (CEGA), with support from the Bill Melinda Gates Foundation.
Estimating Parents’ Valuation on Elite School in China∗

The Chinese economy has rapidly developed over the past 25 years. While the poverty rate declined from 60% in 1978 to 10% in 2014, disparities have grown as people from different socioeconomic classes have benefited from the growth at different rates, leading to an increase in income inequality (Dollar, 2007). As Doepke and Zilibotti (2019) argued, greater income inequality has driven parents to be more attentive toward their children’s educational success. Since the quality of primary education is a key determinant of later academic achievement (Hoekstra et al., 2018), competition for places in the best primary schools in China is increasingly intense. Furthermore, though admission to public primary schools is residence-based in urban areas, homeowners have precedence over renters in the admissions process. In practice, parents must own a property within the school attendance ∗We thank the UCLA Ziman Center for Real Estate’s Rosalinde and Arthur Gilbert Program in Real Estate, Finance and Urban Economics for generous funding. 1Data Source: World Bank.
Unpacking Social Biases: An Analysis of Sense Embeddings Using the Backpack Model

Large Language Models are known to contain bias. These biases are based on stereotypes the model inevitably encounters during training, resulting in word associations between areas such as gender, race, and/or socioeconomic status and descriptive words. As a result, the model not only hurts groups with these overgeneralized beliefs, but the accuracy of many NLP tasks are hurt when their foundations are built on biases. Due to the promise the novel Backpack models are showing, it is imperative that we understand and quantify what kind of biases have made its way into the trained model and find ways to mitigate these biases from occurring in embeddings. Various experiments can be run to accomplish this task. In this project, we conduct word association tests for a sample of selected words that contain bias and evaluate it against the model’s produced sense vectors. We found that our method accurately detects similar words while still producing biased sense vectors. Our ongoing work aims to refine the method and improve the mitigation of bias in LLMs.
Pricing Mortgage Stress: Evidence and Policies

We use the new market of Credit Risk Transfers (CRTs) linked to U.S. mortgages to quantify the extent to which the government-sponsored enterprises subsidize credit risk in mortgages and estimate the impact of this subsidy on the macroeconomy. Taking advantage of the onset of the Covid-19 pandemic, and heterogeneity in CRT exposure to credit risk, we estimate how the private market prices mortgage default risk. Consistent with those estimates, we calibrate a model of credit supply, and estimate the marketimplied guarantee fees (g-fees) of agency mortgages. We estimate the g-fee subsidy that the government-sponsored enterprises gave over the last thirty years, as the di¤erence between the market-implied and the actual g-fees. The subsidy provides countercyclical stabilization. For example, it lowered mortgage rates by 41% during the 2009 nancial crisis. This subsidy increases mortgage originations, housing prices, consumption and GDP.
Efficient Two-stage Approach for Long Document Summarization

Long document summarization is essential in NLP, but state-of-the-art models like BART and BERT face limitations summarizing long documents. Current methods like Longformer Encoder Decoder (LED) can handle longer document summarization but suffers from slower processing times due to their complex attention mechanisms. To address this challenge, we propose a two-stage approach that combines sentence extraction algorithms with BART for generating abstractive summaries. Our approach leverages the efficiency of extraction algorithms to identify key sentences from the input document. BART then generates more coherent and informative abstractive summaries from these extracted sentences. Our experimental results show that our approach is four times more time-efficient than the LED baseline while processing the same amount of data, with approximately the same performance in terms of Rouge 1,2, and L F-measure. With the hope of further improving the generated summaries, we also use Generative Adversarial Network to train the model. Our proposed approach has important implications for NLP applications that require summarization of long text, such as legal documents or scientific papers.
Stress and Coping Strategies Among Parents of Neonates Admitted in Neonatal Intensive Care Unit – a Hospital-Based Study

Background: Parents can experience feelings of guiltiness, helplessness, distress, fear, and anxiety whose neonates are admitted in Neonatal Intensive Care Unit (NICU) creating stressful family patterns and demanding coping mechanisms. This study aimed to assess stress and coping strategies among the parents of neonates admitted to NICU. Methods: A cross-sectional study was conducted among 60 parents of neonates admitted to NICU of BPKIHS, Dharan. Data was collected using Parental Stress Scale: Neonatal Intensive Care Unit (PSS: NICU) for stress score, and Brief Coping Orientation to Problems Experienced (COPE) Inventory for coping score through face-to-face interviews and entered and analyzed data in Statistical Packages for Social Sciences (version 16). Categorical variables were presented as frequency and percentage whereas numeric variables as mean and standard deviation, paired t-test was used to compare mean stress and coping scores of parents. An independent sample t-test was used to compare mean stress score and coping strategies with selected demographic variables. Results: Overall parental mean stress score was 3.14±0.48, higher among mothers compared to fathers (3.39±0.43; 2.89±0.37; p-value <0.001). The overall parental mean coping score was 2.79±0.20 (father: 2.79±0.21; mother: 2.79±0.18; p-value=0.922). The mother's stress score was higher in planned pregnancy (3.47±0.38, p-value=0.04) compared to unplanned (3.11±0.50) and higher in caesarean section (3.55±0.38, p-value=0.04) compared to vaginal delivery (3.23±0.43). The coping score was higher among newborn’s parent with birth-weight ≥ 2.5 kg compared to newborn’s parent with birth-weight < 2.5 kg (father: 2.67±0.23 versus: 2.85±0.19, p-value=0.03; mother: 2.69±0.18 versus 2.83±0.17, p-value=0.04). Conclusion: Neonatal admission to NICU produces a varying degree of stress to the parents primarily due to alteration in a parental role leading to the adoption of various coping strategies.
Finetuning minBERT for Downstream Tasks

Models that can infer semantic understanding from text are applicable to problems spanning many domains. We investigate the application of several techniques to a pretrained BERT model to maximize multitask accuracy when performing sentiment analysis, paraphrase detection, and similarity evaluation. We present implementations of two extensions to the BERT model and apply them to these three tasks. Of these extensions, a novel implementation of question-answering yielded outstanding results.
Time Series Prediction Methodology and Ensemble Model Using Real-World Data

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring.
AJG-22-2171 1108..1112

INTRODUCTION Gastric outlet obstruction (GOO) is a clinical syndrome caused by the blockage of gastric emptying arising from both benign and malignant conditions. It is characterized by abdominal pain, fullness, postprandial vomiting, early satiety, and weight loss. Historically, GOO was a primary complication of peptic ulcer disease, but this has shifted in the era of proton pump inhibitors. Today, it is predominantly a sequela of malignancy. It is a debilitating condition that can lead to malnutrition, interruption in chemotherapy, or delay in surgery. Progression of gastric, duodenal, and pancreaticobiliary (ampullary carcinoma, distal cholangiocarcinoma, or carcinoma of the proximal pancreas) malignancies commonly manifests with GOO from an extrinsic compression or direct infiltration of the primary tumor. The diagnosis is often established by cross-sectional imaging showing a distended stomach in the correct clinical setting. Endoscopy is essential to establish the underlying diagnosis. While retained gastric contents may confirm the clinical syndrome, it is not uncommon for extrinsic compression or early infiltration of the stomach or duodenum to produce delayed emptying well before a complete obstruction. Traditionally, duodenal stenting or surgery has been the mainstay of therapy; however, endoscopic ultrasound-guided gastroenterostomy (EUS-GE) has been a relatively new therapeutic option. Recent guidelines published by the American Gastroenterology Association and the European Society of Gastrointestinal Endoscopy have supported the use of EUS-GE as an alternative in the management of malignant GOO, though the clinical setting remains to be elucidated. This review aims to discuss the management of GOO from pancreaticobiliary malignancy and examine the evolving role of EUS-GE.
SuperBERT: Multi-task Finetuning with Domain Adaptation

BERT is a transformer-based model that pretrains on unsupervised tasks to learn weights that generate rich word representations from input tokens. We aimed to improve BERT’s performance on paraphrase detection, semantic similarity, and semantic analysis tasks in a model called SuperBERT. To do so, we pretrained BERT using masked language modeling on datasets relevant to the three domains. We then used different multi-task learning procedures utilizing gradient surgery on top of the additional pretraining. We found that multi-task learning with gradient surgery leads to significant improvements over the baseline on all of the three tasks. We also found using mean pooling on the token embeddings with cosine similarity as similarity metric further improves the model performance on the semantic similarity task. We finally found additional pretraining on the target domain datasets improved the performance on semantic similarity but did not lead to any improvements on paraphrase detection and sentiment analysis. Thus, we conclude that multi-task finetuning on the three downstream tasks leads to performance on each task that is comparable to a model that finetunes only on the specific task, even with significantly less data.
Automatic Spectral Classification of Stars using Machine Learning: An Approach based on the use of Unbalanced Data

With the increase in astronomical surveys, astronomers are faced with the challenging task of analyzing a large amount of data in order to classify observed objects into hard-to-distinguish classes. This article presents a machine learning-based method for the automatic spectral classification of stars from the latest release of the SDSS database. We propose the combinatorial use of spectral data, derived stellar data, and calculated data to create patterns. Using these patterns as inputs, we develop a Random Forest model that outputs the spectral class of the observed star. Our model is able to classify data into six complex classes: A, F, G, K, M, and Carbon stars. Due to the unbalanced nature of the data, we train our model considering three data use cases: using the original data, using under-sampling, and over-sampling data techniques. We further test our model by using a fixed dataset and a stratified dataset. From this, we analyze the performance of our model through statistical metrics. The experimental results showed that the combinatorial use of data as an input pattern contributes to improve the prediction scores in all data use cases, meanwhile, the model trained with augmented data outperforms the other cases. Our results suggest that machine learning-based spectral classification of stars may be useful for astronomers.
Allometric relationship between diameter at breast height and height of Quercus variabilis plantations in Beijing

In order to test the fractal network model and other three allometric models,the allometric relationship between stem diameter(D) and tree height(H) in Quercus variabilis plantations from the Sizuolou Nature Reserve of Beijing was studied based on the power function H=aDb.The results of reduced major axis(RMA) regression showed that in 12 out of 16 plots,the RMA slopes between logD and logH supported the predicted value(2/3) of elastic similarity and fractal network model,while only 2 plots supported the geometry similarity model and no data supported the stress similarity model;both univariate and multivariate analyses showed that habitat and stand factors did not have significant influence on D-H allometry.This study suggests that the elastic similarity and fractal network model are more suitable for describing the allometric relationship between D and H than geometry similarity or stress similarity model.