pwc-archive/papers-with-abstracts huggingface dataset:
Macroeconomic factors and Stock exchange return: A Statistical Analysis
Authors: Md. Fazlul Huq Khan, Md. Masum Billah
Date: 2023-05-03 00:00:00

The purpose of this research is to examine the relationship between the Dhaka Stock exchange index return and macroeconomic variables such as exchange rate, inflation, money supply etc. The long-term relationship between macroeconomic variables and stock market returns has been analyzed by using the Johnson Cointegration test, Augmented Dicky Fuller (ADF) and Phillip Perron (PP) tests. The results revealed the existence of cointegrating relationship between stock prices and the macroeconomic variables in the Dhaka stock exchange. The consumer price index, money supply, and exchange rates proved to be strongly associated with stock returns, while market capitalization was found to be negatively associated with stock returns. The findings suggest that in the long run, the Dhaka stock exchange is reactive to macroeconomic indicators.
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
Authors: Binbin Xie, Jia Song, Liangying Shao, Suhang Wu, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Jinsong Su
Date: 2023-05-04 00:00:00

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to 167 previous works, achieving greater coverage of this task than previous surveys. Particularly, we focus highly on deep learning-based keyphrase prediction, which attracts increasing attention of this task in recent years. Afterwards, we conduct several groups of experiments to carefully compare representative models. To the best of our knowledge, our work is the first attempt to compare these models using the identical commonly-used datasets and evaluation metric, facilitating in-depth analyses of their disadvantages and advantages. Finally, we discuss the possible research directions of this task in the future.
A Mediation Analysis of the Relationship Between Land Use Regulation Stringency and Employment Dynamics
Authors: Uche Oluku, Shaoming Cheng
Date: 2023-05-03 00:00:00

The paper examines the effects of stringent land use regulations, measured using the Wharton Residential Land Use Regulatory Index (WRLURI), on employment growth during the period 2010-2020 in the Retail, Professional, and Information sectors across 878 local jurisdictions in the United States. All the local jurisdictions exist in both (2006 and 2018) waves of the WRLURI surveys and hence constitute a unique panel data. We apply a mediation analytical framework to decompose the direct and indirect effects of land use regulation stringency on sectoral employment growth and specialization. Our analysis suggests a fully mediated pattern in the relationship between excessive land use regulations and employment growth, with housing cost burden as the mediator. Specifically, a one standard deviation increase in the WRLURI index is associated with an approximate increase of 0.8 percentage point in the proportion of cost burdened renters. Relatedly, higher prevalence of cost-burdened renters has moderate adverse effects on employment growth in two sectors. A one percentage point increase in the proportion of cost burdened renters is associated with 0.04 and 0.017 percentage point decreases in the Professional and Information sectors, respectively.
Don't Trust, Verify: Towards a Framework for the Greening of Bitcoin
Authors: Juan Ignacio Ibañez, Alexander Freier
Date: 2023-05-02 00:00:00

For more than a decade, Bitcoin has gained as much adoption as it has received criticism. Fundamentally, Bitcoin is under fire for the high carbon footprint that results from the energy-intensive proof-of-work (PoW) consensus algorithm. There is a trend however for Bitcoin mining to adopt a trajectory toward achieving carbon-negative status, notably due to the adoption of methane-based mining and mining-based flexible load response (FLR) to complement variable renewable energy (VRE) generation. Miners and electricity sellers may increase their profitability not only by taking advantage of excess energy, but also by selling green tokens to buyers interested in greening their portfolios. Nevertheless, a proper ''green Bitcoin'' accounting system requires a standard framework for the accreditation of sustainable bitcoin holdings. The proper way to build such a framework remains contested. In this paper, we survey the different sustainable Bitcoin accounting systems. Analyzing the various alternatives, we suggest a path forward.
Cooperation and Cognition in Social Networks
Authors: Edoardo Gallo, Joseph Lee, Yohanes Eko Riyanto, Erwin Wong
Date: 2023-05-02 00:00:00

Social networks can sustain cooperation by amplifying the consequences of a single defection through a cascade of relationship losses. Building on Jackson et al. (2012), we introduce a novel robustness notion to characterize low cognitive complexity (LCC) networks - a subset of equilibrium networks that imposes a minimal cognitive burden to calculate and comprehend the consequences of defection. We test our theory in a laboratory experiment and find that cooperation is higher in equilibrium than in non-equilibrium networks. Within equilibrium networks, LCC networks exhibit higher levels of cooperation than non-LCC networks. Learning is essential for the emergence of equilibrium play.
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward
Authors: Washim Uddin Mondal, Vaneet Aggarwal
Date: 2023-05-04 00:00:00
Tasks: Attribute, reinforcement-learning, Reinforcement Learning

We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback. The delay and compositeness of rewards mean that rewards generated as a result of taking an action at a given state are fragmented into different components, and they are sequentially realized at delayed time instances. The partial anonymity attribute implies that a learner, for each state, only observes the aggregate of past reward components generated as a result of different actions taken at that state, but realized at the observation instance. We propose an algorithm named $\mathrm{DUCRL2}$ to obtain a near-optimal policy for this setting and show that it achieves a regret bound of $\tilde{\mathcal{O}}\left(DS\sqrt{AT} + d (SA)^3\right)$ where $S$ and $A$ are the sizes of the state and action spaces, respectively, $D$ is the diameter of the MDP, $d$ is a parameter upper bounded by the maximum reward delay, and $T$ denotes the time horizon. This demonstrates the optimality of the bound in the order of $T$, and an additive impact of the delay.
Non-refunding of VAT to soybean exporters or economic impact of Soybean amendments
Authors: Oleg Nivievskyi, Roman Neyter, Olha Halytsia, Pavlo Martyshev, Oleksandr Donchenko
Date: 2023-04-29 00:00:00

Exempting soybean and rapeseed exporters from VAT has a negative effect on the economy of $\$$44.5-60.5 million per year. The implemented policy aimed to increase the processing of soybeans and rapeseed by Ukrainian plants. As a result, the processors received $\$$26 million and the state budget gained $\$$2-18 million. However, soybean farmers, mostly small and medium-sized, received $ 88.5 million in losses, far outweighing the benefits of processors and the state budget.
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks
Authors: Peng Ye, Tong He, Shengji Tang, Baopu Li, Tao Chen, Lei Bai, Wanli Ouyang
Date: 2023-05-04 00:00:00

Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond their performance limits. Previous research has suggested that residual networks can be considered as ensembles of shallow networks, which implies that the final performance of a residual network is influenced by a group of subnetworks. We identify a previously overlooked problem that is analogous to social loafing, where subnetworks within a residual network are prone to exert less effort when working as part of a group compared to working alone. We define this problem as \textit{network loafing}. Similar to the decreased individual productivity and overall performance as demonstrated in society, network loafing inevitably causes sub-par performance. Inspired by solutions from social psychology, we first propose a novel training scheme called stimulative training, which randomly samples a residual subnetwork and calculates the KL divergence loss between the sampled subnetwork and the given residual network for extra supervision. In order to unleash the potential of stimulative training, we further propose three simple-yet-effective strategies, including a novel KL- loss that only aligns the network logits direction, random smaller inputs for subnetworks, and inter-stage sampling rules. Comprehensive experiments and analysis verify the effectiveness of stimulative training as well as its three improved strategies.
AutoML-GPT: Automatic Machine Learning with GPT
Authors: Shujian Zhang, Chengyue Gong, Lemeng Wu, Xingchao Liu, Mingyuan Zhou
Date: 2023-05-04 00:00:00
Tasks: AutoML

AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in various aspects of reasoning, comprehension, and interaction. Consequently, we propose developing task-oriented prompts and automatically utilizing LLMs to automate the training pipeline. To implement this concept, we present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyperparameters. AutoML-GPT dynamically takes user requests from the model and data cards and composes the corresponding prompt paragraph. Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct the experiments from data processing to model architecture, hyperparameter tuning, and predicted training log. By leveraging {\ours}'s robust language capabilities and the available AI models, AutoML-GPT can tackle numerous intricate AI tasks across various tasks and datasets. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many AI tasks.
Agricultural Policy in Ukraine
Authors: Oleg Nivievskyi, Pavlo Martyshev, Sergiy Kvasha
Date: 2023-04-29 00:00:00

Building upon the theory and methodology of agricultural policy developed in the previous chapter, in Chapter 2 we analyse and assess agricultural policy making in Ukraine since the breakup of Soviet Union till today. Going from top down to the bottom, we begin by describing the evolution of state policy in the agri-food sector. In the beginning, we describe the major milestones of agricultural policy making since independence, paving the way to the political economy of the modern agricultural policy in Ukraine. Then we describe the role of agri-food sector in the national economy as well as globally in ensuring food security in the world. After, we dig deeper and focus on a detailed performance of agricultural sector by looking at farm structures, their land use, overall and sector-wise untapped productivity potential. Modern agricultural policy and institutional set-up is contained and analyzed in details in the section 2.4. A review of the agricultural up- and downstream sectors wraps up this chapter
Revisiting Graph Contrastive Learning for Anomaly Detection
Authors: Zhiyuan Liu, Chunjie Cao, Fangjian Tao, Jingzhang Sun
Date: 2023-05-04 00:00:00
Tasks: Anomaly Detection, Attribute, Contrastive Learning

Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules work have not been fully explored. We dive into the multi-scale and graph augmentation mechanism and observed that multi-scale contrast modules do not enhance the expression, while the multi-GNN modules are the hidden contributors. Previous studies have tended to attribute the benefits brought by multi-GNN to the multi-scale modules. In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective. We extracted two variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed with the low computational cost. The variant M-MAG equipped with multi-GNN modules further improve the detection performance. Our study sheds light on the drawback of the existing GCAD methods and demonstrates the potential of multi-GNN and graph augmentation modules. Our code is available at https://github.com/liuyishoua/MAG-Framework.
Carbon Emission Reduction Effect of RMB Appreciation: Empirical Evidence from 283 Prefecture-Level Cities of China
Authors: Chen Fengxian, Lv Xiaoyao
Date: 2023-04-28 00:00:00

Based on the panel data of 283 prefecture-level cities in China from 2006 to 2019, this paper measures the extent and mechanism of the impact of RMB real effective exchange rate fluctuations on carbon emission intensity. The results show that: (1) For every 1% appreciation of the real effective exchange rate of RMB, the carbon emission intensity decreases by an average of 0.463 tons/10000 yuan; (2) The "carbon emission reduction effect" of RMB real effective exchange rate appreciation is more obvious in the eastern regions, coastal areas, regions with high urbanization levels, and areas with open information; (3) The appreciation of RMB real effective exchange rate can reduce carbon dioxide emission intensity by improving regional R&D and innovation ability, restraining foreign trade and foreign investment, promoting industrial structure optimization and upgrading, and improving income inequality.
Breast Cancer Diagnosis Using Machine Learning Techniques
Authors: Juan Zuluaga-Gomez
Date: 2023-05-04 00:00:00
Tasks: image-classification, Image Classification

Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Latest advances in computational tools, infrared cameras and devices for bio-impedance quantification, have given a chance to emerge other reference techniques like thermography, infrared thermography, electrical impedance tomography and biomarkers found in blood tests, therefore being faster, reliable and cheaper than other methods. In the last two decades, the techniques mentioned above have been considered as parallel and extended approaches for breast cancer diagnosis, as well many authors concluded that false positives and false negatives rates are significantly reduced. Moreover, when a screening method works together with a computational technique, it generates a "computer-aided diagnosis" system. The present work aims to review the last breakthroughs about the three techniques mentioned earlier, suggested machine learning techniques to breast cancer diagnosis, thus, describing the benefits of some methods in relation with other ones, such as, logistic regression, decision trees, random forest, deep and convolutional neural networks. With this, we studied several hyperparameters optimization approaches with parzen tree optimizers to improve the performance of baseline models. An exploratory data analysis for each database and a benchmark of convolutional neural networks for the database of thermal images are presented. The benchmark process, reviews image classification techniques with convolutional neural networks, like, Resnet50, NasNetmobile, InceptionResnet and Xception.
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain
Authors: Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Reza Haffari, Van Nguyen, Shuang Yu
Date: 2023-05-04 00:00:00
Tasks: Articles, Knowledge Graphs

International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely related to physical movement (i.e., those generated by physical sensors, or hard data) is not sufficient. This has led to research and development efforts aimed at combining hard data with other types of data (especially human-generated or soft data). Existing work often assumes that input soft data is available in a structured format, or is focused on extracting certain relevant entities or concepts to accompany or annotate hard data. Much less attention has been given to extracting the rich knowledge about the situations of interest implicitly embedded in the large amount of soft data existing in unstructured formats (such as intelligence reports and news articles). In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i.e., in the form of probabilistic knowledge graphs). This will increase the accuracy of and confidence in, the extracted knowledge and facilitate subsequent reasoning and learning. To this end, we propose Maritime DeepDive, an initial prototype for the automated construction of probabilistic knowledge graphs from natural language data for the maritime domain. In this paper, we report on the current implementation of Maritime DeepDive, together with preliminary results on extracting probabilistic events from maritime piracy incidents. This pipeline was evaluated on a manually crafted gold standard, yielding promising results.
Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
Authors: Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, Rui Yan
Date: 2023-05-03 00:00:00
Tasks: Abstractive Text Summarization, Dialogue Generation, Machine Translation, Retrieval, Retrieval-augmented Generation, Text Generation, Text Summarization

With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory would typically prompt better generation~(we define this as primal problem). The traditional approach for memory retrieval involves selecting memory that exhibits the highest similarity to the input. However, this method is constrained by the quality of the fixed corpus from which memory is retrieved. In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round. This enables the model to leverage its own output, referred to as self-memory, for improved generation. We evaluate the effectiveness of selfmem on three distinct text generation tasks: neural machine translation, abstractive text summarization, and dialogue generation, under two generation paradigms: fine-tuned small model and few-shot LLM. Our approach achieves state-of-the-art results in four directions in JRC-Acquis, XSum (50.3 ROUGE-1), and BigPatent (62.9 ROUGE-1), demonstrating the potential of self-memory in enhancing retrieval-augmented generation models. Furthermore, we conduct thorough analyses of each component in the selfmem framework to identify bottlenecks and provide insights for future research.
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Authors: Lichang Chen, Heng Huang, Minhao Cheng
Date: 2023-05-03 00:00:00
Tasks: Natural Language Understanding

Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. However, the existing prompt tuning methods have training instability issues, as the variance of scores under different random seeds is quite large. To address this critical problem, we first investigate and find that the loss landscape of vanilla prompt tuning is precipitous when it is visualized, where a slight change of input data can cause a big fluctuation in the loss landscape. This is an essential factor that leads to the instability of prompt tuning. Based on this observation, we introduce perturbation-based regularizers, which can smooth the loss landscape, into prompt tuning. We propose a new algorithm, called Prompt Tuning with Perturbation-based regularizer~(PTP), which can not only alleviate training instability dramatically but also boost the performance of prompt tuning. We design two kinds of perturbation-based regularizers, including random-noise-based and adversarial-based. In particular, our proposed perturbations are flexible on both text space and embedding space. Extensive experiments show the effectiveness of our proposed methods in stabilizing the training. Our new algorithms improve the state-of-the-art prompt tuning methods by 1.94\% and 2.34\% on SuperGLUE and FewGLUE benchmarks, respectively.
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
Authors: Yue Wu, So Yeon Min, Yonatan Bisk, Ruslan Salakhutdinov, Amos Azaria, Yuanzhi Li, Tom Mitchell, Shrimai Prabhumoye
Date: 2023-05-03 00:00:00
Tasks: Instruction Following

Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.