pwc-archive/papers-with-abstracts huggingface dataset:
Coarse Personalization
Authors: Walter W. Zhang, Sanjog Misra
Date: 2022-04-12 00:00:00
Tasks: Management, Marketing

Advances in estimating heterogeneous treatment effects enable firms to personalize marketing mix elements and target individuals at an unmatched level of granularity, but feasibility constraints limit such personalization. In practice, firms choose which unique treatments to offer and which individuals to offer these treatments with the goal of maximizing profits: we call this the coarse personalization problem. We propose a two-step solution that makes segmentation and targeting decisions in concert. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes by utilizing treatment effects to choose which unique treatments to offer and who to assign to these treatments. We show that a combination of available machine learning tools for estimating heterogeneous treatment effects and a novel application of optimal transport methods provides a viable and efficient solution. With data from a large-scale field experiment for promotions management, we find that our methodology outperforms extant approaches that segment on consumer characteristics or preferences and those that only search over a prespecified grid. Using our procedure, the firm recoups over 99.5% of its expected incremental profits under fully granular personalization while offering only five unique treatments. We conclude by discussing how coarse personalization arises in other domains.
Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach
Authors: Maaz Mahadi, Tarig Ballal, Muhammad Moinuddin, Tareq Y. Al-Naffouri, Ubaid Al-Saggaf
Date: 2022-04-12 00:00:00
Tasks: Portfolio Optimization

This paper is concerned with optimizing the global minimum-variance portfolio's (GMVP) weights in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing the GMVP weights is highly influenced by the data covariance matrix estimation. In a high-dimensional setting, it is well known that the sample covariance matrix is not a proper estimator of the true covariance matrix since it is not invertible when we have fewer observations than the data dimension. Even with more observations, the sample covariance matrix may not be well-conditioned. This paper determines the GMVP weights based on a regularized covariance matrix estimator to overcome the aforementioned difficulties. Unlike other methods, the proper selection of the regularization parameter is achieved by minimizing the mean-squared error of an estimate of the noise vector that accounts for the uncertainty in the data mean estimation. Using random-matrix-theory tools, we derive a consistent estimator of the achievable mean-squared error that allows us to find the optimal regularization parameter using a simple line search. Simulation results demonstrate the effectiveness of the proposed method when the data dimension is larger than the number of data samples or of the same order.
Neyman allocation is minimax optimal for best arm identification with two arms
Authors: Karun Adusumilli
Date: 2022-04-12 00:00:00

This note describes the optimal policy rule, according to the local asymptotic minimax regret criterion, for best arm identification when there are only two treatments. It is shown that the optimal sampling rule is the Neyman allocation, which allocates a constant fraction of units to each treatment in a manner that is proportional to the standard deviation of the treatment outcomes. When the variances are equal, the optimal ratio is one-half. This policy is independent of the data, so there is no adaptation to previous outcomes. At the end of the experiment, the policy maker adopts the treatment with higher average outcomes.
Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data
Authors: Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Date: 2022-04-12 00:00:00
Tasks: Density Estimation

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation analysis. We apply the proposed method to the tabulated summary data of the U.S. tax returns to estimate the income distribution.
A critical assessment of the National Entrepreneurship Context Index of the Global Entrepreneurship Monitor
Authors: Cornelius A. Rietveld, Pankaj C. Patel
Date: 2022-04-12 00:00:00

Data collected through the National Expert Survey (NES) of the Global Entrepreneurship Monitor (GEM) are widely used to assess the quality and impact of national entrepreneurial ecosystems. By focusing on the measurement of the National Entrepreneurship Context Index (NECI), we argue and show that the subjective nature of the responses of the national experts precludes meaningful cross-country analyses and cross-country rankings. Moreover, we show that the limited precision of the NECI severely constraints the longitudinal assessment of within-country trends. We provide recommendations for the current use of NECI data and suggestions for future NES data collections.
Impact of an Employment Policy on Companies' Expectations Fulfilment
Authors: Javier Espinosa-Brito, Carlos Yevenes-Ortega, Gonzalo Franetovic-Guzman, Diana Ochoa-Diaz
Date: 2022-04-12 00:00:00

We study the effect of Chile's Employment Protection Law (Ley de Protecci\'on del Empleo, EPL), a law which allowed temporal suspensions of job contracts in exceptional circumstances during the COVID-19 pandemic, on the fulfillment of firms' expectations regarding layoffs. We use monthly surveys directed at a representative group of firms in the national territory. This panel data allows to follow firms through time and analyze the match between their expectations and the actual realization to model their expectation fulfilment. We model the probability of expectation fulfilment through a logit model that allows for moderation effects. Results suggest that for those firms that expected to fire workers, for the firms that used the EPL, the odds they finally ended up with a job separation are 50% of the odds for those that did not used the EPL. Small firms increase their probability of expectation fulfilment in 11.9% when using the EPL compared to large firms if they declared they were expecting to fire workers.
On the Potential of Using Sub-THz Frequencies for Beyond 5G
Authors: Oskari Tervo, Ilmari Nousiainen, Ismael Peruga Nasarre, Esa Tiirola, Jari Hulkkonen
Date: 2022-04-12 00:00:00

This paper studies the potential of using above 71GHz frequencies for 5G-Advanced or later in 6G. More specifically, the focus is to analyze what could be needed in terms of waveform and numerologies. The results suggest that higher baseline subcarrier spacings (SCSs) may be needed when moving above 71GHz, to fulfill the need for higher required bandwidths and phase noise robustness. The required SCS depends on carrier frequency and modulation order. It is also illustrated that single-carrier waveforms, especially Known Tail Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing (KT-DFT-s-OFDM) waveform is a potential candidate to be used in 5G-Advanced or 6G for sub-THz frequencies due to its robustness to phase noise, lower output power back-off and flexible adaptation of head and tail lengths.
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm
Authors: Navid Ayoobi, Elnaz Banan Sadeghian
Date: 2022-04-12 00:00:00
Tasks: Brain Computer Interface, Decoder, EEG, Electroencephalogram (EEG), Motor Imagery, Onset Detection, Time Series, Time Series Analysis, Time Series Prediction

In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown. To detect these onsets, most self-paced approaches apply a window function on the continuous EEG signal and split it into long segments for further analysis. As a result, the system has a high latency. To reduce the system latency, we propose an algorithm based on the time series prediction concept and use the data of the previously received time samples to predict the upcoming time samples. Our predictor is an encoder-decoder (ED) network built with long short-term memory (LSTM) units. The onsets of the MI commands are detected shortly by comparing the incoming signal with the predicted signal. The proposed method is validated on dataset IVc from BCI competition III. The simulation results show that the proposed algorithm improves the average F1-score achieved by the winner of the competition by 26.7% for latencies shorter than one second.
Leveraging Deep Neural Networks for Massive MIMO Data Detection
Authors: Ly V. Nguyen, Nhan T. Nguyen, Nghi H. Tran, Markku Juntti, A. Lee Swindlehurst, Duy H. N. Nguyen
Date: 2022-04-11 00:00:00

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.
Leveraging massively parallel reporter assays for evolutionary questions
Authors: Irene Gallego Romero, Amanda J. Lea
Date: 2022-04-12 00:00:00
Tasks: Diversity

A long-standing goal of evolutionary biology is to decode how gene regulatory processes contribute to organismal diversity, both within and between species. This question has remained challenging to answer, due both to the difficulties of predicting function from non-coding sequence, and to the technological constraints of laboratory research with non-model taxa. However, a recent methodological development in functional genomics, the massively parallel reporter assay (MPRA), makes it possible to test thousands to millions of sequences for regulatory activity in a single in vitro experiment. It does so by combining traditional, single-locus episomal reporter assays (e.g., luciferase reporter assays) with the scalability of high-throughput sequencing. In this perspective, we discuss the execution, advantages, and limitations of MPRAs for research in evolutionary biology. We review recent studies that have made use of this approach to address explicitly evolutionary questions, highlighting study designs that we believe are particularly well-positioned to gain from MPRA approaches. Additionally, we propose solutions for extending these powerful assays to rare taxa and those with limited genomic resources. In doing so, we underscore the broad potential of MPRAs to drive genome-scale functional evolutionary genetics studies in non-traditional model organisms.
Bayesian Parameter Estimation for Dynamical Models in Systems Biology
Authors: Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Date: 2022-04-11 00:00:00
Tasks: parameter estimation, Uncertainty Quantification

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.
Nonlinear dynamics of an epidemic compartment model with asymptomatic infections and mitigation
Authors: Maurice Görtz, Joachim Krug
Date: 2022-04-12 00:00:00

A significant proportion of the infections driving the current {SARS-CoV-2} pandemic are transmitted asymptomatically. Here we introduce and study a simple epidemic model with separate compartments comprising asymptomatic and symptomatic infected individuals. The linear dynamics determining the outbreak condition of the model is equivalent to a renewal theory approach with exponential waiting time distributions. Exploiting a nontrivial conservation law of the full nonlinear dynamics, we derive analytic bounds on the peak number of infections in the absence and presence of mitigation through isolation and testing. The bounds are compared to numerical solutions of the differential equations.
Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
Authors: Lei Fang, Junren Li, Ming Zhao, Li Tan, Jian-Guang Lou
Date: 2022-04-12 00:00:00
Tasks: Decision Making, Machine Translation, Retrosynthesis, Text Generation

Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generation or machine translation models in natural language processing were frequently utilized approaches. The token-by-token decoding approach is not intuitive from a chemistry perspective because some substructures are relatively stable and remain unchanged during reactions. In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach. Our approach achieved improvement over previously reported models, and we find that the performance can be further boosted if the accuracy of substructure extraction is improved. The substructures extracted by our approach can provide users with better insights for decision-making compared to existing methods. We hope this work will generate interest in this fast growing and highly interdisciplinary area on retrosynthesis prediction and other related topics.
Ensemble learning for portfolio valuation and risk management
Authors: Lotfi Boudabsa, Damir Filipović
Date: 2022-04-12 00:00:00
Tasks: Ensemble Learning, Management, regression

We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. The method can also be applied to Bermudan style options. Numerical experiments show good results in moderate dimension problems.
Optimal brokerage contracts in Almgren-Chriss model with multiple clients
Authors: Guillermo Alonso Alvarez, Sergey Nadtochiy, Kevin Webster
Date: 2022-04-11 00:00:00

This paper constructs optimal brokerage contracts for multiple (heterogeneous) clients trading a single asset whose price follows the Almgren-Chriss model. The distinctive features of this work are as follows: (i) the reservation values of the clients are determined endogenously, and (ii) the broker is allowed to not offer a contract to some of the potential clients, thus choosing her portfolio of clients strategically. We find a computationally tractable characterization of the optimal portfolios of clients (up to a digital optimization problem, which can be solved efficiently if the number of potential clients is small) and conduct numerical experiments which illustrate how these portfolios, as well as the equilibrium profits of all market participants, depend on the price impact coefficients.
Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets
Authors: Narayana Darapaneni, Anwesh Reddy Paduri, Himank Sharma, Milind Manjrekar, Nutan Hindlekar, Pranali Bhagat, Usha Aiyer, Yogesh Agarwal
Date: 2022-04-07 00:00:00
Tasks: Sentiment Analysis, Stock Market Prediction, Stock Price Prediction

Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.
Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision
Authors: Duygu Ider, Stefan Lessmann
Date: 2022-04-06 00:00:00
Tasks: Articles

Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribute to this literature by introducing weak learning, a recently proposed NLP approach to address the problem that text data is unlabeled. Without a dependent variable, it is not possible to finetune pretrained NLP models on a custom corpus. We confirm that finetuning using weak labels enhances the predictive value of text-based features and raises forecast accuracy in the context of predicting cryptocurrency returns. More fundamentally, the modeling paradigm we present, weak labeling domain-specific text and finetuning pretrained NLP models, is universally applicable in (financial) forecasting and unlocks new ways to leverage text data.
Discovering material information using hierarchical Reformer model on financial regulatory filings
Authors: Francois Mercier, Makesh Narsimhan
Date: 2022-03-28 00:00:00
Tasks: BIG-bench Machine Learning, Sentence

Most applications of machine learning for finance are related to forecasting tasks for investment decisions. Instead, we aim to promote a better understanding of financial markets with machine learning techniques. Leveraging the tremendous progress in deep learning models for natural language processing, we construct a hierarchical Reformer ([15]) model capable of processing a large document level dataset, SEDAR, from canadian financial regulatory filings. Using this model, we show that it is possible to predict trade volume changes using regulatory filings. We adapt the pretraining task of HiBERT ([36]) to obtain good sentence level representations using a large unlabelled document dataset. Finetuning the model to successfully predict trade volume changes indicates that the model captures a view from financial markets and processing regulatory filings is beneficial. Analyzing the attention patterns of our model reveals that it is able to detect some indications of material information without explicit training, which is highly relevant for investors and also for the market surveillance mandate of financial regulators.
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
Authors: Shubhankar Borse, Hyojin Park, Hong Cai, Debasmit Das, Risheek Garrepalli, Fatih Porikli
Date: 2022-04-11 00:00:00
Proceeding: CVPR 2022 1
Tasks: Panoptic Segmentation, Segmentation

This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation. In existing works, it is common to use a shared backbone to extract features for both things (countable classes such as vehicles) and stuff (uncountable classes such as roads). This, however, fails to capture the rich relations among them, which can be utilized to enhance visual understanding and segmentation performance. To address this shortcoming, we propose a novel Panoptic, Instance, and Semantic Relations (PISR) module to exploit such contexts. First, we generate panoptic encodings to summarize key features of the semantic classes and predicted instances. A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone. It produces a feature map that captures 1) the relations across semantic classes and instances and 2) the relations between these panoptic categories and spatial features. PISR also automatically learns to focus on the more important instances, making it robust to the number of instances used in the relational attention module. Moreover, PISR is a general module that can be applied to any existing panoptic segmentation architecture. Through extensive evaluations on panoptic segmentation benchmarks like Cityscapes, COCO, and ADE20K, we show that PISR attains considerable improvements over existing approaches.