pwc-archive/papers-with-abstracts huggingface dataset:
FlexiTerm: A more efficient implementation of flexible multi-word term recognition
Authors: Irena Spasic
Date: 2021-10-13 00:00:00

Terms are linguistic signifiers of domain-specific concepts. Automated recognition of multi-word terms (MWT) in free text is a sequence labelling problem, which is commonly addressed using supervised machine learning methods. Their need for manual annotation of training data makes it difficult to port such methods across domains. FlexiTerm, on the other hand, is a fully unsupervised method for MWT recognition from domain-specific corpora. Originally implemented in Java as a proof of concept, it did not scale well, thus offering little practical value in the context of big data. In this paper, we describe its re-implementation in Python and compare the performance of these two implementations. The results demonstrated major improvements in terms of efficiency, which allow FlexiTerm to transition from the proof of concept to the production-grade application.
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
Authors: Sharon Levy, Kevin Mo, Wenhan Xiong, William Yang Wang
Date: 2021-10-13 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Diversity, Misinformation, Open-Domain Question Answering, Question Answering, Re-Ranking

Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, leading to the spread of misinformation. This has created the need for a public space for users to ask questions and receive credible, scientific answers. To fulfill this need, we turn to the task of open-domain question-answering, which we can use to efficiently find answers to free-text questions from a large set of documents. In this work, we present such a system for the emergent domain of COVID-19. Despite the small data size available, we are able to successfully train the system to retrieve answers from a large-scale corpus of published COVID-19 scientific papers. Furthermore, we incorporate effective re-ranking and question-answering techniques, such as document diversity and multiple answer spans. Our open-domain question-answering system can further act as a model for the quick development of similar systems that can be adapted and modified for other developing emergent domains.
FILM: Following Instructions in Language with Modular Methods
Authors: So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov
Date: 2021-10-12 00:00:00
Proceeding: ICLR 2022 4
Tasks: Imitation Learning, Instruction Following

Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46 %) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49 %). Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions.
Divide-and-Conquer Fusion
Authors: Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts
Date: 2021-10-14 00:00:00

Combining several (sample approximations of) distributions, which we term sub-posteriors, into a single distribution proportional to their product, is a common challenge. Occurring, for instance, in distributed 'big data' problems, or when working under multi-party privacy constraints. Many existing approaches resort to approximating the individual sub-posteriors for practical necessity, then find either an analytical approximation or sample approximation of the resulting (product-pooled) posterior. The quality of the posterior approximation for these approaches is poor when the sub-posteriors fall out-with a narrow range of distributional form, such as being approximately Gaussian. Recently, a Fusion approach has been proposed which finds an exact Monte Carlo approximation of the posterior, circumventing the drawbacks of approximate approaches. Unfortunately, existing Fusion approaches have a number of computational limitations, particularly when unifying a large number of sub-posteriors. In this paper, we generalise the theory underpinning existing Fusion approaches, and embed the resulting methodology within a recursive divide-and-conquer sequential Monte Carlo paradigm. This ultimately leads to a competitive Fusion approach, which is robust to increasing numbers of sub-posteriors.
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion
Authors: Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang
Date: 2021-10-12 00:00:00
Tasks: Matrix Completion, Missing Elements

The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broad images since different images favor different features captured by different explicit regularizations. As such, this paper proposes a new adaptive and implicit low-rank regularization that captures the low-rank prior dynamically from the training data. The core of our new adaptive and implicit low-rank regularization is parameterizing the Laplacian matrix in the Dirichlet energy-based regularization, which we call the regularization \textit{AIR}. Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training. We validate AIR's effectiveness on various benchmark tasks, indicating that the AIR is particularly favorable for the scenarios when the missing entries are non-uniform. The code can be found at https://github.com/lizhemin15/AIR-Net
Web Search via an Efficient and Effective Brain-Machine Interface
Authors: Xuesong Chen, Ziyi Ye, Xiaohui Xie, Yiqun Liu, Weihang Su, Shuqi Zhu, Min Zhang, Shaoping Ma
Date: 2021-10-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades. With the maturity of the Brain-Machine Interface, we build an efficient and effective communication system between human beings and search engines based on electroencephalogram(EEG) signals, called Brain-Machine Search Interface(BMSI) system. The BMSI system provides functions including query reformulation and search result interaction. In our system, users can perform search tasks without having to use the mouse and keyboard. Therefore, it is useful for application scenarios in which hand-based interactions are infeasible, e.g, for users with severe neuromuscular disorders. Besides, based on brain signals decoding, our system can provide abundant and valuable user-side context information(e.g., real-time satisfaction feedback, extensive context information, and a clearer description of information needs) to the search engine, which is hard to capture in the previous paradigm. In our implementation, the system can decode user satisfaction from brain signals in real-time during the interaction process and re-rank the search results list based on user satisfaction feedback. The demo video is available at http://www.thuir.cn/group/YQLiu/datasets/BMSISystem.mp4.
Presenting a Larger Up-to-date Movie Dataset and Investigating the Effects of Pre-released Attributes on Gross Revenue
Authors: Arnab Sen Sharma, Tirtha Roy, Sadique Ahmmod Rifat, Maruf Ahmed Mridul
Date: 2021-10-13 00:00:00

Movie-making has become one of the most costly and risky endeavors in the entertainment industry. Continuous change in the preference of the audience makes it harder to predict what kind of movie will be financially successful at the box office. So, it is no wonder that cautious, intelligent stakeholders and large production houses will always want to know the probable revenue that will be generated by a movie before making an investment. Researchers have been working on finding an optimal strategy to help investors in making the right decisions. But the lack of a large, up-to-date dataset makes their work harder. In this work, we introduce an up-to-date, richer, and larger dataset that we have prepared by scraping IMDb for researchers and data analysts to work with. The compiled dataset contains the summery data of 7.5 million titles and detail information of more than 200K movies. Additionally, we perform different statistical analysis approaches on our dataset to find out how a movie's revenue is affected by different pre-released attributes such as budget, runtime, release month, content rating, genre etc. In our analysis, we have found that having a star cast/director has a positive impact on generated revenue. We introduce a novel approach for calculating the star power of a movie. Based on our analysis we select a set of attributes as features and train different machine learning algorithms to predict a movie's expected revenue. Based on generated revenue, we classified the movies in 10 categories and achieved a one-class-away accuracy rate of almost 60% (bingo accuracy of 30%). All the generated datasets and analysis codes are available online. We also made the source codes of our scraper bots public, so that researchers interested in extending this work can easily modify these bots as they need and prepare their own up-to-date datasets.
Queuing model of axonal transport
Authors: Paul C Bressloff
Date: 2021-10-13 00:00:00
Tasks: model, Unity

The motor-driven intracellular transport of vesicles to synaptic targets in the axons and dendrites of neurons plays a crucial role in normal cell function. Moreover, stimulus-dependent regulation of active transport is an important component of long-term synaptic plasticity, whereas the disruption of vesicular transport can lead to the onset of various neurodegenerative diseases. In this paper we investigate how the discrete and stochastic nature of vesicular transport in axons contributes to fluctuations in the accumulation of resources within synaptic targets. We begin by solving the first passage time problem of a single motor-cargo complex (particle) searching for synaptic targets distributed along a one-dimensional axonal cable. We then use queuing theory to analyze the accumulation of synaptic resources under the combined effects of multiple search-and-capture events and degradation. In particular, we determine the steady-state mean and variance of the distribution of synaptic resources along the axon in response to the periodic insertion of particles. The mean distribution recovers the spatially decaying distribution of resources familiar from deterministic population models. However, the discrete nature of vesicular transport can lead to Fano factors that are greater than unity (non-Poissonian) across the array of synapses, resulting in significant fluctuation bursts. We also find that each synaptic Fano factor is independent of the rate of particle insertion but increases monotonically with the amount of protein cargo in each vesicle. This implies that fluctuations can be reduced by increasing the injection rate while decreasing the cargo load of each vesicle.
OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution
Authors: Fabrizio Angaroni, Marco Antoniotti, Alex Graudenzi
Date: 2021-10-13 00:00:00
Tasks: Benchmarking

Algorithmic strategies for the spatio-temporal simulation of multi-cellular systems are crucial to generate synthetic datasets for bioinformatics tools benchmarking, as well as to investigate experimental hypotheses on real-world systems in a variety of in-silico scenarios. In particular, efficient algorithms are needed to overcome the harsh trade-off between scalability and expressivity, which typically limits our capability to produce realistic simulations, especially in the context of cancer evolution. We introduce the Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE), a computational framework for the simulation of the spatio-temporal evolution of cancer subpopulations and of the experimental procedures of both bulk andsingle-cell sequencing. OG-SPACE relies on an evolution of the Gillespie algorithm optimized to deal with large numbers of cells and is designed tohandle a variety of birth-death processes and interaction rules on arbitrary lattices. As output OG-SPACE returns: the visual snapshots of the spatial configuration of the system over time, the phylogeny of the (sampled) cells, the mutational tree, the variant allele frequency spectrum (for bulk experiments) and the cell genotypes (for single-cell experiments).OG-SPACE is freely available at:https://github.com/BIMIB-DISCo/OG-SPACE
The Neuroscience of Advanced Scientific Concepts
Authors: Robert A. Mason, Reinhard A. Schumacher, Marcel A. Just
Date: 2021-10-12 00:00:00
Tasks: Management

Cognitive neuroscience methods can identify the fMRI-measured neural representation of familiar individual concepts, such as apple, and decompose them into meaningful neural and semantic components. This approach was applied here to determine the neural representations and underlying dimensions of representation of far more abstract physics concepts related to matter and energy, such as fermion and dark matter, in the brains of 10 Carnegie Mellon physics faculty members who thought about the main properties of each of the concepts. One novel dimension coded the measurability vs. immeasurability of a concept. Another novel dimension of representation evoked particularly by post-classical concepts was associated with four types of cognitive processes, each linked to particular brain regions: (1) Reasoning about intangibles, taking into account their separation from direct experience and observability; (2) Assessing consilience with other, firmer knowledge; (3) Causal reasoning about relations that are not apparent or observable; and (4) Knowledge management of a large knowledge organization consisting of a multi-level structure of other concepts. Two other underlying dimensions, previously found in physics students, periodicity, and mathematical formulation, were also present in this faculty sample. The data were analyzed using factor analysis of stably responding voxels, a Gaussian-na\"ive Bayes machine-learning classification of the activation patterns associated with each concept, and a regression model that predicted activation patterns associated with each concept based on independent ratings of the dimensions of the concepts. The findings indicate that the human brain systematically organizes novel scientific concepts in terms of new dimensions of neural representation.
Choice probabilities and correlations in closed-form route choice models: specifications and drawbacks
Authors: Fiore Tinessa, Vittorio Marzano, Andrea Papola
Date: 2021-10-14 00:00:00
Tasks: Form

This paper investigates the performance, in terms of choice probabilities and correlations, of existing and new specifications of closed-form route choice models with flexible correlation patterns, namely the Link Nested Logit (LNL), the Paired Combinatorial Logit (PCL) and the more recent Combination of Nested Logit (CoNL) models. Following a consolidated track in the literature, choice probabilities and correlations of the Multinomial Probit (MNP) model by (Daganzo and Sheffi, 1977) are taken as target. Laboratory experiments on small/medium-size networks are illustrated, also leveraging a procedure for practical calculation of correlations of any GEV models, proposed by (Marzano 2014). Results show that models with inherent limitations in the coverage of the domain of feasible correlations yield unsatisfactory performance, whilst the specifications of the CoNL proposed in the paper appear the best in fitting both MNP correlations and probabilities. Performance of the models are appreciably ameliorated by introducing lower bounds to the nesting parameters. Overall, the paper provides guidance for the practical application of tested models.
Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction
Authors: Mahdieh Yazdani
Date: 2021-10-14 00:00:00
Tasks: BIG-bench Machine Learning

In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.
Partial Identification of Marginal Treatment Effects with discrete instruments and misreported treatment
Authors: Santiago Acerenza
Date: 2021-10-12 00:00:00

This paper provides partial identification results for the marginal treatment effect ($MTE$) when the binary treatment variable is potentially misreported and the instrumental variable is discrete. Identification results are derived under different sets of nonparametric assumptions. The identification results are illustrated in identifying the marginal treatment effects of food stamps on health.
ESG and Sovereign Risk: What is Priced in by the Bond Market and Credit Rating Agencies?
Authors: Raphaël Semet, Thierry Roncalli, Lauren Stagnol
Date: 2021-10-13 00:00:00

In this paper, we examine the materiality of ESG on country creditworthiness from a credit risk and fundamental analysis viewpoint. We first determine the ESG indicators that are most relevant when it comes to explaining the sovereign bond yield, after controlling the effects of traditional fundamental variables such as economic strength and credit rating. We also emphasize the major themes that are directly useful for investors when assessing the country risk premium. At the global level, we notice that these themes mainly belong to the E and G pillars. Those results confirm that extra-financial criteria are integrated into bond pricing. However, we also identify a clear difference between high-and middle-income countries. Indeed, whereas the S pillar is lagging for the highest income countries, it is nearly as important as the G pillar for the middle-income ones. Second, we determine which ESG metrics are indirectly valuable for assessing a country's solvency. More precisely, we attempt to infer credit rating solely from extra-financial criteria, that is the ESG indicators that are priced in by credit rating agencies. We find that there is no overlap between the set of indicators that predict credit ratings and those that directly explain sovereign bond yields. The results also highlight the importance of the G and S pillars when predicting credit ratings. The E pillar is lagging, suggesting that credit rating agencies are undermining the impact of climate change and environmental topics on country creditworthiness. This is consistent with the traditional view that social and governance issues are the main drivers of the sovereign risk, because they are more specific and less global than environmental issues. Finally, taking these different results together, this research shows that opposing extra-financial and fundamental analysis does not make a lot of sense.
Appointments: A More Effective Commitment Device for Health Behaviors
Authors: Laura Derksen, Jason Kerwin, Natalia Ordaz Reynoso, Olivier Sterck
Date: 2021-10-09 00:00:00

Health behaviors are plagued by self-control problems, and commitment devices are frequently proposed as a solution. We show that a simple alternative works even better: appointments. We randomly offer HIV testing appointments and financial commitment devices to high-risk men in Malawi. Appointments are much more effective than financial commitment devices, more than doubling testing rates. In contrast, most men who take up financial commitment devices lose their investments. Appointments address procrastination without the potential drawback of commitment failure, and also address limited memory problems. Appointments have the potential to increase demand for healthcare in the developing world.
Group Identity, Social Learning and Opinion Dynamics
Authors: Sebastiano Della Lena, Luca Paolo Merlino
Date: 2021-10-14 00:00:00

In this paper, we study opinion dynamics in a balanced social structure consisting of two groups. Agents learn the true state of the world naively learning from their neighbors and from an unbiased source of information. Agents want to agree with others of the same group--in-group identity -- but to disagree with those of the opposite group--out-group conflict. We characterize the long-run opinions, and show that agents' influence depends on their Bonacich centrality in the signed network of opinion exchange. Finally, we study the effect of group size, the weight given to unbiased information and homophily when agents in the same group are homogeneous.
Maskin Meets Abreu and Matsushima
Authors: Yi-Chun Chen, Takashi Kunimoto, Yifei Sun, Siyang Xiong
Date: 2021-10-13 00:00:00

The theory of full implementation has been criticized for using integer/modulo games which admit no equilibrium (Jackson (1992)). To address the critique, we revisit the classical Nash implementation problem due to Maskin (1999) but allow for the use of lotteries and monetary transfers as in Abreu and Matsushima (1992, 1994). We unify the two well-established but somewhat orthogonal approaches in full implementation theory. We show that Maskin monotonicity is a necessary and sufficient condition for (exact) mixed-strategy Nash implementation by a finite mechanism. In contrast to previous papers, our approach possesses the following features: finite mechanisms (with no integer or modulo game) are used; mixed strategies are handled explicitly; neither undesirable outcomes nor transfers occur in equilibrium; the size of transfers can be made arbitrarily small; and our mechanism is robust to information perturbations. Finally, our result can be extended to infinite/continuous settings and ordinal settings.