pwc-archive/papers-with-abstracts huggingface dataset:
Solution for the EPO CodeFest on Green Plastics: Hierarchical multi-label classification of patents relating to green plastics using deep learning
Authors: Tingting Qiao, Gonzalo Moro Perez
Date: 2023-02-22 00:00:00
Tasks: Classification, Hierarchical Multi-label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This work aims at hierarchical multi-label patents classification for patents disclosing technologies related to green plastics. This is an emerging field for which there is currently no classification scheme, and hence, no labeled data is available, making this task particularly challenging. We first propose a classification scheme for this technology and a way to learn a machine learning model to classify patents into the proposed classification scheme. To achieve this, we come up with a strategy to automatically assign labels to patents in order to create a labeled training dataset that can be used to learn a classification model in a supervised learning setting. Using said training dataset, we come up with two classification models, a SciBERT Neural Network (SBNN) model and a SciBERT Hierarchical Neural Network (SBHNN) model. Both models use a BERT model as a feature extractor and on top of it, a neural network as a classifier. We carry out extensive experiments and report commonly evaluation metrics for this challenging classification problem. The experiment results verify the validity of our approach and show that our model sets a very strong benchmark for this problem. We also interpret our models by visualizing the word importance given by the trained model, which indicates the model is capable to extract high-level semantic information of input documents. Finally, we highlight how our solution fulfills the evaluation criteria for the EPO CodeFest and we also outline possible directions for future work. Our code has been made available at https://github.com/epo/CF22-Green-Hands
ChatGPT: A Meta-Analysis after 2.5 Months
Authors: Christoph Leiter, Ran Zhang, Yanran Chen, Jonas Belouadi, Daniil Larionov, Vivian Fresen, Steffen Eger
Date: 2023-02-20 00:00:00
Tasks: Chatbot, Ethics

ChatGPT, a chatbot developed by OpenAI, has gained widespread popularity and media attention since its release in November 2022. However, little hard evidence is available regarding its perception in various sources. In this paper, we analyze over 300,000 tweets and more than 150 scientific papers to investigate how ChatGPT is perceived and discussed. Our findings show that ChatGPT is generally viewed as of high quality, with positive sentiment and emotions of joy dominating in social media. Its perception has slightly decreased since its debut, however, with joy decreasing and (negative) surprise on the rise, and it is perceived more negatively in languages other than English. In recent scientific papers, ChatGPT is characterized as a great opportunity across various fields including the medical domain, but also as a threat concerning ethics and receives mixed assessments for education. Our comprehensive meta-analysis of ChatGPT's current perception after 2.5 months since its release can contribute to shaping the public debate and informing its future development. We make our data available.
A Surrogate-Assisted Highly Cooperative Coevolutionary Algorithm for Hyperparameter Optimization in Deep Convolutional Neural Network
Authors: An Chen, Zhigang Ren, Muyi Wang, Hui Chen, Haoxi Leng, Shuai Liu
Date: 2023-02-25 00:00:00
Tasks: Hyperparameter Optimization, image-classification, Image Classification

Convolutional neural networks (CNNs) have gained remarkable success in recent years. However, their performance highly relies on the architecture hyperparameters, and finding proper hyperparameters for a deep CNN is a challenging optimization problem owing to its high-dimensional and computationally expensive characteristics. Given these difficulties, this study proposes a surrogate-assisted highly cooperative hyperparameter optimization (SHCHO) algorithm for chain-styled CNNs. To narrow the large search space, SHCHO first decomposes the whole CNN into several overlapping sub-CNNs in accordance with the overlapping hyperparameter interaction structure and then cooperatively optimizes these hyperparameter subsets. Two cooperation mechanisms are designed during this process. One coordinates all the sub-CNNs to reproduce the information flow in the whole CNN and achieve macro cooperation among them, and the other tackles the overlapping components by simultaneously considering the involved two sub-CNNs and facilitates micro cooperation between them. As a result, a proper hyperparameter configuration can be effectively located for the whole CNN. Besides, SHCHO also employs the well-performing surrogate technique to assist in the hyperparameter optimization of each sub-CNN, thereby greatly reducing the expensive computational cost. Extensive experimental results on two widely-used image classification datasets indicate that SHCHO can significantly improve the performance of CNNs.
Resistance Maintained in Digital Organisms despite Guanine/Cytosine-Based Fitness Cost and Extended De-Selection: Implications to Microbial Antibiotics Resistance
Authors: Clarence FG Castillo, Zhu En Chay, Maurice HT Ling
Date: 2023-02-19 00:00:00

Antibiotics resistance has caused much complication in the treatment of diseases, where the pathogen is no longer susceptible to specific antibiotics and the use of such antibiotics are no longer effective for treatment. A recent study that utilizes digital organisms suggests that complete elimination of specific antibiotic resistance is unlikely after the disuse of antibiotics, assuming that there are no fitness costs for maintaining resistance once resistance are established. Fitness cost are referred to as reaction to change in environment, where organism improves its' abilities in one area at the expense of the other. Our goal in this study is to use digital organisms to examine the rate of gain and loss of resistance where fitness costs have incurred in maintaining resistance. Our results showed that GC-content based fitness cost during de-selection by removal of antibiotic-induced selective pressure portrayed similar trends in resistance compared to that of no fitness cost, at all stages of initial selection, repeated de-selection and re-introduction of selective pressure. Paired t-test suggested that prolonged stabilization of resistance after initial loss is not statistically significant for its difference to that of no fitness cost. This suggests that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics despite presence of fitness cost in maintaining antibiotic resistance during the disuse of antibiotics, once a resistant pool of micro-organism has been established.
Random forests for binary geospatial data
Authors: Arkajyoti Saha, Abhirup Datta
Date: 2023-02-27 00:00:00
Tasks: Gaussian Processes

The manuscript develops new method and theory for non-linear regression for binary dependent data using random forests. Existing implementations of random forests for binary data cannot explicitly account for data correlation common in geospatial and time-series settings. For continuous outcomes, recent work has extended random forests (RF) to RF-GLS that incorporate spatial covariance using the generalized least squares (GLS) loss. However, adoption of this idea for binary data is challenging due to the use of the Gini impurity measure in classification trees, which has no known extension to model dependence. We show that for binary data, the GLS loss is also an extension of the Gini impurity measure, as the latter is exactly equivalent to the ordinary least squares (OLS) loss. This justifies using RF-GLS for non-parametric mean function estimation for binary dependent data. We then consider the special case of generalized mixed effects models, the traditional statistical model for binary geospatial data, which models the spatial random effects as a Gaussian process (GP). We propose a novel link-inversion technique that embeds the RF-GLS estimate of the mean function from the first step within the generalized mixed effects model framework, enabling estimation of non-linear covariate effects and offering spatial predictions. We establish consistency of our method, RF-GP, for both mean function and covariate effect estimation. The theory holds for a general class of stationary absolutely regular dependent processes that includes common choices like Gaussian processes with Mat\'ern or compactly supported covariances and autoregressive processes. The theory relaxes the common assumption of additive mean functions and accounts for the non-linear link. We demonstrate that RF-GP outperforms competing methods for estimation and prediction in both simulated and real-world data.
Learning coherences from nonequilibrium fluctuations in a quantum heat engine
Authors: Manash Jyoti Sarmah, Himangshu Prabal Goswami
Date: 2023-02-27 00:00:00

We develop an efficient machine learning protocol to predict the noise-induced coherence from the nonequilibrium fluctuations of photon exchange statistics in a quantum heat engine. The engine is a four-level quantum system coupled to a unimodal quantum cavity. The nonequilibrium fluctuations correspond to the work done during the photon exchange process between the four-level system and the cavity mode. We specifically evaluate the mean, variance, skewness, and kurtosis for a range of engine parameters using a full counting statistical approach combined with a quantum master equation technique. We use these numerically evaluated cumulants as input data to successfully predict the hot bath induced coherence. A supervised machine learning technique based on K-Nearest Neighbor(KNN) is found to work better than a variety of learning models that we tested.
Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation
Authors: Xiaoshu Chen, Xiangsheng Li, Kunliang Wei, Bin Hu, Lei Jiang, Zeqian Huang, Zhanhui Kang
Date: 2023-02-27 00:00:00

Eliminating examination bias accurately is pivotal to apply click-through data to train an unbiased ranking model. However, most examination-bias estimators are limited to the hypothesis of Position-Based Model (PBM), which supposes that the calculation of examination bias only depends on the rank of the document. Recently, although some works introduce information such as clicks in the same query list and contextual information when calculating the examination bias, they still do not model the impact of document representation on search engine result pages (SERPs) that seriously affects one's perception of document relevance to a query when examining. Therefore, we propose a Multi-Feature Integration Model (MFIM) where the examination bias depends on the representation of document except the rank of it. Furthermore, we mine a key factor slipoff counts that can indirectly reflects the influence of all perception-bias factors. Real world experiments on Baidu-ULTR dataset demonstrate the superior effectiveness and robustness of the new approach. The source code is available at \href{https://github.com/lixsh6/Tencent_wsdm_cup2023/tree/main/pytorch_unbias}{https://github.com/lixsh6/Tencent\_wsdm\_cup2023}
Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
Authors: Xiangsheng Li, Xiaoshu Chen, Kunliang Wei, Bin Hu, Lei Jiang, Zeqian Huang, Zhanhui Kang
Date: 2023-02-27 00:00:00
Tasks: Document Ranking, Information Retrieval, Language Modeling, Language Modelling, Retrieval

Pre-trained language models have achieved great success in various large-scale information retrieval tasks. However, most of pretraining tasks are based on counterfeit retrieval data where the query produced by the tailored rule is assumed as the user's issued query on the given document or passage. Therefore, we explore to use large-scale click logs to pretrain a language model instead of replying on the simulated queries. Specifically, we propose to use user behavior features to pretrain a debiased language model for document ranking. Extensive experiments on Baidu desensitization click logs validate the effectiveness of our method. Our team on WSDM Cup 2023 Pre-training for Web Search won the 1st place with a Discounted Cumulative Gain @ 10 (DCG@10) score of 12.16525 on the final leaderboard.
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval
Authors: Zhiqi Huang, Puxuan Yu, James Allan
Date: 2023-02-26 00:00:00
Tasks: Information Retrieval, Machine Translation, Retrieval, Transfer Learning

In this paper, we introduce the approach behind our submission for the MIRACL challenge, a WSDM 2023 Cup competition that centers on ad-hoc retrieval across 18 diverse languages. Our solution contains two neural-based models. The first model is a bi-encoder re-ranker, on which we apply a cross-lingual distillation technique to transfer ranking knowledge from English to the target language space. The second model is a cross-encoder re-ranker trained on multilingual retrieval data generated using neural machine translation. We further fine-tune both models using MIRACL training data and ensemble multiple rank lists to obtain the final result. According to the MIRACL leaderboard, our approach ranks 8th for the Test-A set and 2nd for the Test-B set among the 16 known languages.
Enzyme kinetics simulation at the scale of individual particles
Authors: Taylor Kearney, Mark B. Flegg
Date: 2023-02-27 00:00:00

Enzyme-catalysed reactions involve two distinct timescales. There is a short timescale on which enzymes bind to substrate molecules to produce bound complexes, and a comparatively long timescale on which the complex is transformed into a product. The rate at which the substrate is converted into product is characteristically non-linear and is traditionally derived by applying singular perturbation theory to the system's governing equations. Central to this analysis is the assumption that complex formation is effectively instantaneous on the timescale over which significant substrate degradation occurs. This prevents accurate modelling of enzyme kinetics by many particle-based simulations of reaction-diffusion systems as they rely on proximity-based reaction conditions that do not correctly model the fast reactions associated with the complex on the long timescale. In this paper we derive a new proximity-based reaction condition that correctly incorporates the reactions that occur on the short timescale for a specific enzymatic system. We present proof of concept particle-based simulations and demonstrate that non-linear reaction rates typical of enzyme kinetics can be reproduced without needing to explicitly simulate reactions on the short timescale.
Trade-off between reproduction and mobility prolongs organisms' survival in rock-paper-scissors models
Authors: J. Menezes, E. Rangel
Date: 2023-02-26 00:00:00

We study the spatial rock-paper-scissors model, where resource competitors' cyclic dominance impacts organisms' energy levels. Our model assumes that failed selection interactions can lead to energy loss, reducing the chances of success in the spatial game and hastening decline. To prevent death by energy insufficiency, organisms of one out of the species strategically perform a trade-off between reproduction and mobility. When prioritising exploring more extensive areas, organisms aim to maximise the chances of acquiring resources to regain high energy levels. Through simulation, we examine the effect of survival behaviour on species segregation and spatial patterns. Our outcomes show that the trade-off between offspring generation and accelerated movement effectively protects individuals from death due to lack of energy. Moreover, the risk of being eliminated by an enemy in the cyclic game reduces due to the behavioural strategy. Considering a three-state model, we quantify how the trade-off parameter controls the organisms' energy recovery. Computing the median organisms' survival time, we find that although individuals performing the trade-off strategy may live longer, the organisms of other species are negatively affected by a life expectancy reduction. Our research may elucidate the role of adaptive survival strategies in species persistence and provide valuable insights for ecologists.
Determining Molecular Complexity using Assembly Theory and Spectroscopy
Authors: Michael Jirasek, Abhishek Sharma, Jessica R. Bame, S. Hessam M. Mehr, Nicola Bell, Stuart M. Marshall, Cole Mathis, Alasdair Macleod, Geoffrey J. T. Cooper, Marcel Swart, Rosa Mollfulleda, Leroy Cronin
Date: 2023-02-24 00:00:00

Determining the complexity of molecules has important applications from molecular design to understanding the history of the process that led to the formation of the molecule. Currently, it is not possible to experimentally determine, without full structure elucidation, how complex a molecule is. Assembly Theory has been developed to quantify the complexity of a molecule by finding the shortest path to construct the molecule from building blocks, revealing its molecular assembly index (MA). In this study, we present an approach to rapidly and exhaustively calculate the MA of molecules from the spectroscopic measurements. We demonstrate that molecular complexity (MA) can be experimentally estimated using three independent techniques: nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS), and infrared spectroscopy (IR), and these give consistent results with good correlations with the theoretically determined values from assembly theory. By identifying and analysing the number of absorbances in IR spectra, carbon resonances in NMR, or molecular fragments in tandem MS, the molecular assembly of an unknown molecule can be reliably estimated from experimental data. This represents the first experimentally quantifiable approach to defining molecular assembly, a reliable metric for complexity, as an intrinsic property of molecules and can also be performed on complex mixtures. This paves the way to use spectroscopic and spectrometric techniques to unambiguously detect alien life in the solar system, and beyond on exoplanets.
Heterogeneous source model for magnetoenecephalography
Authors: Takaaki Nara, Ten-yu Yang, Kenta Kabashima
Date: 2023-02-24 00:00:00
Tasks: model

In this paper, we propose a novel source model for a magnetoencephalography (MEG) inverse problem that combines a conventional extended parametric approach and an imaging approach.Our aim is to separately identify a focal current source and background activities spread over the brain.The new source model consists of two terms to represent different spatial characteristics: one is a localized patch source represented with a few parameters based on a mapping from a sphere to the cortex surface,and the other is a distributed source expressed using elemental dipoles on grid points on the cortical surface. We call it a heterogeneous source model, because these two models have not been used simultaneously.Effectiveness of the proposed method is shown via numerical simulations.
Forecasting Macroeconomic Tail Risk in Real Time: Do Textual Data Add Value?
Authors: Philipp Adämmer, Jan Prüser, Rainer Schüssler
Date: 2023-02-27 00:00:00

We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions of employment, output, inflation and consumer sentiment in a high-dimensional setting. Our results suggest that news data contain valuable information that is not captured by a large set of economic indicators. We provide empirical evidence that this information can be exploited to improve tail risk predictions. The added value is largest when media coverage and sentiment are combined to compute text-based predictors. Methods that capture quantile-specific non-linearities produce overall superior forecasts relative to methods that feature linear predictive relationships. The results are robust along different modeling choices.
Multicell experiments for marginal treatment effect estimation of digital ads
Authors: Caio Waisman, Brett R. Gordon
Date: 2023-02-27 00:00:00
Tasks: Experimental Design

Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance extant empirical approaches may not produce the estimands a decision maker needs to solve the problem of interest. For example, these experimental designs are common in digital advertising settings but typical methods do not yield effects that inform the intensive margin: how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches.
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think
Authors: Ziwei Mei, Liugang Sheng, Zhentao Shi
Date: 2023-02-27 00:00:00
Tasks: regression, Time Series Analysis

Local Projection is widely used for impulse response estimation, with the Fixed Effect (FE) estimator being the default for panel data. This paper highlights the presence of Nickell bias for all regressors in the FE estimator, even if lagged dependent variables are absent in the regression. This bias is the consequence of the inherent panel predictive specification. We recommend using the split-panel jackknife estimator to eliminate the asymptotic bias and restore the standard statistical inference. Revisiting three macro-finance studies on the linkage between financial crises and economic contraction, we find that the FE estimator substantially underestimates the post-crisis economic losses.
Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies
Authors: Sascha A. Keweloh, Mathias Klein, Jan Prüser
Date: 2023-02-25 00:00:00

Different proxy variables used in fiscal policy SVARs lead to contradicting conclusions regarding the size of fiscal multipliers. We show that the conflicting results are due to violations of the exogeneity assumptions, i.e. the commonly used proxies are endogenously related to the structural shocks. We propose a novel approach to include proxy variables into a Bayesian non-Gaussian SVAR, tailored to accommodate for potentially endogenous proxy variables. Using our model, we show that increasing government spending is a more effective tool to stimulate the economy than reducing taxes.
The global economic impact of AI technologies in the fight against financial crime
Authors: James Bell
Date: 2023-02-27 00:00:00
Tasks: Ethics

Is the rapid adoption of Artificial Intelligence a sign that creative destruction (a capitalist innovation process first theorised in 1942) is occurring? Although its theory suggests that it is only visible over time in aggregate, this paper devises three hypotheses to test its presence on a macro level and research methods to produce the required data. This paper tests the theory using news archives, questionnaires, and interviews with industry professionals. It considers the risks of adopting Artificial Intelligence, its current performance in the market and its general applicability to the role. The results suggest that creative destruction is occurring in the AML industry despite the activities of the regulators acting as natural blockers to innovation. This is a pressurised situation where current-generation Artificial Intelligence may offer more harm than benefit. For managers, this papers results suggest that safely pursuing AI in AML requires having realistic expectations of Artificial Intelligence's benefits combined with using a framework for AI Ethics.