pwc-archive/papers-with-abstracts huggingface dataset:
BookSum: A Collection of Datasets for Long-form Narrative Summarization
Authors: Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev
Date: 2021-05-18 00:00:00
Tasks: Abstractive Text Summarization, Form, Long-Form Narrative Summarization, Text Summarization

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.
SHARE: a System for Hierarchical Assistive Recipe Editing
Authors: Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley
Date: 2021-05-17 00:00:00
Tasks: Recipe Generation

The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset -- 83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints -- demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.
Multi-Modal Image Captioning for the Visually Impaired
Authors: Hiba Ahsan, Nikita Bhalla, Daivat Bhatt, Kaivankumar Shah
Date: 2021-05-17 00:00:00
Proceeding: NAACL 2021 4
Tasks: Image Captioning

One of the ways blind people understand their surroundings is by clicking images and relying on descriptions generated by image captioning systems. Current work on captioning images for the visually impaired do not use the textual data present in the image when generating captions. This problem is critical as many visual scenes contain text. Moreover, up to 21% of the questions asked by blind people about the images they click pertain to the text present in them. In this work, we propose altering AoANet, a state-of-the-art image captioning model, to leverage the text detected in the image as an input feature. In addition, we use a pointer-generator mechanism to copy the detected text to the caption when tokens need to be reproduced accurately. Our model outperforms AoANet on the benchmark dataset VizWiz, giving a 35% and 16.2% performance improvement on CIDEr and SPICE scores, respectively.
A deep learning approach for inverse design of the metasurface for dual-polarized waves
Authors: Fardin Ghorbani, Javad Shabanpour, Sina Beyraghi, Hossein Soleimani, Homayoon Oraizi, Mohammad Soleimani
Date: 2021-05-12 00:00:00

Compared to the conventional metasurface design, machine learning-based methods have recently created an inspiring platform for an inverse realization of the metasurfaces. Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves. To automatically generate metasurfaces in a wide range of working frequencies from 4 to 45 GHz, we deliberately design an 8 ring-shaped pattern in such a way that the unit-cells generated in the dataset can produce single or multiple notches in the desired working frequency band. Compared to the general approach, whereby the final metasurface structure may be formed by any randomly distributed "0" and "1", we propose here a restricted output structure. By restricting the output, the number of calculations will be reduced and the learning speed will be increased. Moreover, we have shown that the accuracy of the network reaches 91\%. Obtaining the final unit cell directly without any time-consuming optimization algorithms for both TE and TM polarized waves, and high average accuracy, promises an effective strategy for the metasurface design; thus, the designer is required only to focus on the design goal.
Explainable Graph-based Search for Lessons-Learned Documents in the Semiconductor Industry
Authors: Hasan Abu-Rasheed, Christian Weber, Johannes Zenkert, Roland Krumm, Madjid Fathi
Date: 2021-05-18 00:00:00
Tasks: Management

Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi- to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert design-engineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.
Path-based Deep Network for Candidate Item Matching in Recommenders
Authors: Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang
Date: 2021-05-18 00:00:00
Tasks: Diversity, Recommendation Systems, Retrieval

The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevant items from billion-scale item corpus in low latency and computational cost. Item-to-item collaborative filter (item-based CF) and embedding-based retrieval (EBR) have been long used in the industrial matching stage owing to its efficiency. However, item-based CF is hard to meet personalization, while EBR has difficulty in satisfying diversity. In this paper, we propose a novel matching architecture, Path-based Deep Network (named PDN), which can incorporate both personalization and diversity to enhance matching performance. Specifically, PDN is comprised of two modules: Trigger Net and Similarity Net. PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information. The final relevance between the user and the target item is calculated by explicitly considering user's diverse interests, \ie aggregating the relevance weights of the related two-hop paths (one hop of a path corresponds to user-item interaction and the other to item-item relevance). Furthermore, we describe the architecture design of a matching system with the proposed PDN in a leading real-world E-Commerce service (Mobile Taobao App). Based on offline evaluations and online A/B test, we show that PDN outperforms the existing solutions for the same task. The online results also demonstrate that PDN can retrieve more personalized and more diverse relevant items to significantly improve user engagement. Currently, PDN system has been successfully deployed at Mobile Taobao App and handling major online traffic.
Incorporating Social Welfare in Program-Evaluation and Treatment Choice
Authors: Debopam Bhattacharya, Tatiana Komarova
Date: 2021-05-18 00:00:00

The econometric literature on treatment-effects typically takes functionals of outcome-distributions as `social welfare' and ignores program-impacts on unobserved utilities. We show how to incorporate aggregate utility within econometric program-evaluation and optimal treatment-targeting for a heterogenous population. In the practically important setting of discrete-choice, under unrestricted preference-heterogeneity and income-effects, the indirect-utility distribution becomes a closed-form functional of average demand. This enables nonparametric cost-benefit analysis of policy-interventions and their optimal targeting based on planners' redistributional preferences. For ordered/continuous choice, utility-distributions can be bounded. Our methods are illustrated with Indian survey-data on private-tuition, where income-paths of usage-maximizing subsidies differ significantly from welfare-maximizing ones.
Identification robust inference for moments based analysis of linear dynamic panel data models
Authors: Maurice J. G. Bun, Frank Kleibergen
Date: 2021-05-18 00:00:00
Tasks: Time Series, Time Series Analysis

We use identification robust tests to show that difference, level and non-linear moment conditions, as proposed by Arellano and Bond (1991), Arellano and Bover (1995), Blundell and Bond (1998) and Ahn and Schmidt (1995) for the linear dynamic panel data model, do not separately identify the autoregressive parameter when its true value is close to one and the variance of the initial observations is large. We prove that combinations of these moment conditions, however, do so when there are more than three time series observations. This identification then solely results from a set of, so-called, robust moment conditions. These robust moments are spanned by the combined difference, level and non-linear moment conditions and only depend on differenced data. We show that, when only the robust moments contain identifying information on the autoregressive parameter, the discriminatory power of the Kleibergen (2005) LM test using the combined moments is identical to the largest rejection frequencies that can be obtained from solely using the robust moments. This shows that the KLM test implicitly uses the robust moments when only they contain information on the autoregressive parameter.
Double robust inference for continuous updating GMM
Authors: Frank Kleibergen, Zhaoguo Zhan
Date: 2021-05-18 00:00:00

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the pseudo-true value of the structural parameters in the generalized method of moments. The pseudo-true value is defined as the minimizer of the population continuous updating objective function and equals the true value of the structural parameter in the absence of misspecification.\nocite{hhy96} The (bounding) chi-squared limiting distribution of the DRLM statistic is robust to both misspecification and weak identification of the structural parameters, hence its name. To emphasize its importance for applied work, we use the DRLM test to analyze the return on education, which is often perceived to be weakly identified, using data from Card (1995) where misspecification occurs in case of treatment heterogeneity; and to analyze the risk premia associated with risk factors proposed in Adrian et al. (2014) and He et al. (2017), where both misspecification and weak identification need to be addressed.
Peer Effects in Random Consideration Sets
Authors: Nail Kashaev, Natalia Lazzati
Date: 2019-04-14 00:00:00

We develop a dynamic model of discrete choice that incorporates peer effects into random consideration sets. We characterize the equilibrium behavior and study the empirical content of the model. In our setup, changes in the choices of friends affect the distribution of the consideration sets. We exploit this variation to recover the ranking of preferences, attention mechanisms, and network connections. These nonparametric identification results allow unrestricted heterogeneity across people and do not rely on the variation of either covariates or the set of available options. Our methodology leads to a maximum-likelihood estimator that performs well in simulations. We apply our results to an experimental dataset that has been designed to study the visual focus of attention.
Learning from Forecast Errors: A New Approach to Forecast Combinations
Authors: Tae-Hwy Lee, Ekaterina Seregina
Date: 2020-11-04 00:00:00

Forecasters often use common information and hence make common mistakes. We propose a new approach, Factor Graphical Model (FGM), to forecast combinations that separates idiosyncratic forecast errors from the common errors. FGM exploits the factor structure of forecast errors and the sparsity of the precision matrix of the idiosyncratic errors. We prove the consistency of forecast combination weights and mean squared forecast error estimated using FGM, supporting the results with extensive simulations. Empirical applications to forecasting macroeconomic series shows that forecast combination using FGM outperforms combined forecasts using equal weights and graphical models without incorporating factor structure of forecast errors.
A Tale of Two Tails: A Model-free Approach to Estimating Disaster Risk Premia and Testing Asset Pricing Models
Authors: Tjeerd de Vries
Date: 2021-05-18 00:00:00
Tasks: quantile regression

I introduce a model-free methodology to assess the impact of disaster risk on the market return. Using S&P500 returns and the risk-neutral quantile function derived from option prices, I employ quantile regression to estimate local differences between the conditional physical and risk-neutral distributions. The results indicate substantial disparities primarily in the left-tail, reflecting the influence of disaster risk on the equity premium. These differences vary over time and persist beyond crisis periods. On average, the bottom 5% of returns contribute to 17% of the equity premium, shedding light on the Peso problem. I also find that disaster risk increases the stochastic discount factor's volatility. Using a lower bound observed from option prices on the left-tail difference between the physical and risk-neutral quantile functions, I obtain similar results, reinforcing the robustness of my findings.
Improvements to Modern Portfolio Theory based models applied to electricity systems
Authors: Gabriel Malta Castro, Claude Klöckl, Peter Regner, Johannes Schmidt, Amaro Olimpio Pereira Jr
Date: 2021-05-17 00:00:00

With the increase of variable renewable energy sources (VRES) share in electricity systems, manystudies were developed in order to determine their optimal technological and spatial mix. Modern PortfolioTheory (MPT) has been frequently applied in this context. However, some crucial aspects, important inenergy planning, are not addressed by these analyses. We, therefore, propose several improvements andevaluate how each change in formulation impacts results. More specifically, we address generation costs, system demand, and firm energy output, present a formal model and apply it to the case of Brazil. Wefound that, after including our proposed modifications, the resulting efficient frontier differs strongly fromthe one obtained in the original formulation. Portfolios with high output standard deviation are not ableto provide a firm output level at competitive costs. Furthermore, we show that diversification plays animportant role in smoothing output from VRES portfolios
A time-domain nearfield frequency-invariant beamforming method
Authors: Fei Ma, Thushara D. Abhayapala, Prasanga N. Samarasinghe
Date: 2021-05-18 00:00:00
Tasks: Speech Enhancement

Most existing beamforming methods are frequency-domain methods, and are designed for enhancing a farfield target source over a narrow frequency band. They have found diverse applications and are still under active development. However, they struggle to achieve desired performance if the target source is in the nearfield with a broadband output. This paper proposes a time-domain nearfield frequency-invariant beamforming method. The time-domain implementation makes the beamformer output suitable for further use by real-time applications, the nearfield focusing enables the beamforming method to suppress an interference even if it is in the same direction as the target source, and the frequency-invariant beampattern makes the beamforming method suitable for enhancing the target source over a broad frequency band. These three features together make the beamforming method suitable for real-time broadband nearfield source enhancement, such as speech enhancement in room environments. The beamformer design process is separated from the sound field measurement process, and such that a designed beamformer applies to sensor arrays with various structures. The beamformer design process is further simplified by decomposing it into several independent parts. Simulation results confirm the performance of the proposed beamforming method.
Imaging through fog using quadrature lock-in discrimination
Authors: Shashank Kumar, Bapan Debnath, Meena M. S., Julien Fade, Sankar Dhar, Mehdi Alouini, Fabien Bretenaker, Hema Ramachandran
Date: 2021-05-17 00:00:00

We report experiments conducted in the field in the presence of fog, that were aimed at imaging under poor visibility. By means of intensity modulation at the source and two-dimensional quadrature lock-in detection by software at the receiver, a significant enhancement of the contrast-to-noise ratio was achieved in the imaging of beacons over hectometric distances. Further by illuminating the field of view with a modulated source, the technique helped reveal objects that were earlier obscured due to multiple scattering of light. This method, thus, holds promise of aiding in various forms of navigation under poor visibility due to fog.
Inplace knowledge distillation with teacher assistant for improved training of flexible deep neural networks
Authors: Alexey Ozerov, Ngoc Duong
Date: 2021-05-18 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation

Deep neural networks (DNNs) have achieved great success in various machine learning tasks. However, most existing powerful DNN models are computationally expensive and memory demanding, hindering their deployment in devices with low memory and computational resources or in applications with strict latency requirements. Thus, several resource-adaptable or flexible approaches were recently proposed that train at the same time a big model and several resource-specific sub-models. Inplace knowledge distillation (IPKD) became a popular method to train those models and consists in distilling the knowledge from a larger model (teacher) to all other sub-models (students). In this work a novel generic training method called IPKD with teacher assistant (IPKD-TA) is introduced, where sub-models themselves become teacher assistants teaching smaller sub-models. We evaluated the proposed IPKD-TA training method using two state-of-the-art flexible models (MSDNet and Slimmable MobileNet-V1) with two popular image classification benchmarks (CIFAR-10 and CIFAR-100). Our results demonstrate that the IPKD-TA is on par with the existing state of the art while improving it in most cases.
Beampattern and Robust Doppler Filter Design for Spatial Modulation Based Joint RadCom Systems
Authors: Jifa Zhang
Date: 2021-05-17 00:00:00

Joint radar and communication (RadCom) systems have been proposed to achieve the spectrum sharing between radar and communication in recent years. However, the joint RadCom systems cause the clutter modulation and the performance degradation of both radar and communication. As a consequence, it's very critical to improve the performance of both radar and communication when designing joint RadCom systems. Firstly, this paper designs the constant modulus dual function waveforms for spatial modulation based joint RadCom systems. In order to solve the nonsmooth and nonconvex optimization problem, we propose a method based on alternating direction method of multipliers (ADMM) to obtain a suboptimal solution. Secondly, this paper analyses the effect of beampattern variation on clutter modulation for spatial modulation based joint RadCom systems. Besides, this paper considers the design of robust doppler filter for joint RadCom systems when the receive temporal steering vector is mismatched to the desired temporal steering vector. This paper aims to maximize the worst signal-to-interference-plus-noise-ratio (SINR) of the doppler filter in the specific doppler interval under the similarity constraint and constant white noise gain constraint. In order to solve the nonconvex optimization problem, we relax the original optimization problem to a semidefinite programming (SDP), which is convex and can be solved using off-the-shelf optimization solvers. What's more, the rank-one decomposition method is utilized to synthesize the weight vector of the proposed robust doppler filter. Finally, lots of simulation results are presented to show the robustness of the designed doppler filter.
Optimal Portfolio with Power Utility of Absolute and Relative Wealth
Authors: Andrey Sarantsev
Date: 2021-05-17 00:00:00
Tasks: Portfolio Optimization

Portfolio managers often evaluate performance relative to benchmark, usually taken to be the Standard & Poor 500 stock index fund. This relative portfolio wealth is defined as the absolute portfolio wealth divided by wealth from investing in the benchmark (including reinvested dividends). The classic Merton problem for portfolio optimization considers absolute portfolio wealth. We combine absolute and relative wealth in our new utility function. We also consider the case of multiple benchmarks. To both absolute and relative wealth, we apply power utility functions, possibly with different exponents. We obtain an explicit solution and compare it to the classic Merton solution. We apply our results to the Capital Asset Pricing Model setting.