pwc-archive/papers-with-abstracts huggingface dataset:
A study of computational and conceptual complexities of compartment and agent based models
Authors: Prateek Kunwar, Oleksandr Markovichenko, Monique Chyba, Yuriy Mileyko, Alice Koniges, Thomas Lee
Date: 2021-08-26 00:00:00
Tasks: Decision Making

The ongoing COVID-19 pandemic highlights the essential role of mathematical models in understanding the spread of the virus along with a quantifiable and science-based prediction of the impact of various mitigation measures. Numerous types of models have been employed with various levels of success. This leads to the question of what kind of a mathematical model is most appropriate for a given situation. We consider two widely used types of models: equation-based models (such as standard compartmental epidemiological models) and agent-based models. We assess their performance by modeling the spread of COVID-19 on the Hawaiian island of Oahu under different scenarios. We show that when it comes to information crucial to decision making, both models produce very similar results. At the same time, the two types of models exhibit very different characteristics when considering their computational and conceptual complexity. Consequently, we conclude that choosing the model should be mostly guided by available computational and human resources.
Topological Approximate Bayesian Computation for Parameter Inference of an Angiogenesis Model
Authors: Thomas Thorne, Paul D. W. Kirk, Heather A. Harrington
Date: 2021-08-26 00:00:00
Tasks: Topological Data Analysis

Inferring the parameters of models describing biological systems is an important problem in the reverse engineering of the mechanisms underlying these systems. Much work has focused on parameter inference of stochastic and ordinary differential equation models using Approximate Bayesian Computation (ABC). While there is some recent work on inference in spatial models, this remains an open problem. Simultaneously, advances in topological data analysis (TDA), a field of computational mathematics, have enabled spatial patterns in data to be characterised. Here we focus on recent work using topological data analysis to study different regimes of parameter space for a well-studied model of angiogenesis. We propose a method for combining TDA with ABC to infer parameters in the Anderson-Chaplain model of angiogenesis. We demonstrate that this topological approach outperforms ABC approaches that use simpler statistics based on spatial features of the data. This is a first step towards a general framework of spatial parameter inference for biological systems, for which there may be a variety of filtrations, vectorisations, and summary statistics to be considered. All code used to produce our results is available as a Snakemake workflow.
Cooling Measures and Housing Wealth: Evidence from Singapore
Authors: Wolfgang Karl Härdle, Rainer Schulz, Taojun Sie
Date: 2021-08-26 00:00:00

Excessive house price growth was at the heart of the financial crisis in 2007/08. Since then, many countries have added cooling measures to their regulatory frameworks. It has been found that these measures can indeed control price growth, but no one has examined whether this has adverse consequences for the housing wealth distribution. We examine this for Singapore, which started in 2009 to target price growth over ten rounds in total. We find that welfare from housing wealth in the last round might not be higher than before 2009. This depends on the deflator used to convert nominal into real prices. Irrespective of the deflator, we can reject that welfare increased monotonically over the different rounds.
Unihedge -- A decentralized market prediction platform
Authors: Marko Corn, Nejc Rožman
Date: 2021-08-26 00:00:00
Tasks: Prediction

Unihedge is a decentralized platform for prediction markets with a novel approach. Using Harberger Tax (HTAX) economic policies a new type of prediction market, named HTAX prediction market, was build. HTAX prediction market derivates from Dynamic PariMutuel (DPM) type of prediction markets thus offering its users an unlimited liquidity for any preferred time horizon. It tries to solve some problems of DPM by introducing a new incentive mechanism to support early information incorporation and a protection against share readjustment for hedgers. In the paper also implementation of platform on Ethereum Virtual Machine (EVM) is presented with the usage of Decentralized Exchange (DEX) as an price discovery mechanism for prediction market resolutions.
Market Crash Prediction Model for Markets in A Rational Bubble
Authors: HyeonJun Kim
Date: 2021-08-23 00:00:00
Tasks: Sensitivity

Renowned method of log-periodic power law(LPPL) is one of the few ways that a financial market crash could be predicted. Alongside with LPPL, this paper propose a novel method of stock market crash using white box model derived from simple assumptions about the state of rational bubble. By applying this model to Dow Jones Index and Bitcoin market price data, it is shown that the model successfully predicts some major crashes of both markets, implying the high sensitivity and generalization abilities of the model.
A Time-Varying Network for Cryptocurrencies
Authors: Li Guo, Wolfgang Karl Härdle, Yubo Tao
Date: 2021-08-26 00:00:00
Tasks: Clustering

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.
With One Voice: Composing a Travel Voice Assistant from Re-purposed Models
Authors: Shachaf Poran, Gil Amsalem, Amit Beka, Dmitri Goldenberg
Date: 2021-08-04 00:00:00
Tasks: BIG-bench Machine Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Speech-to-Text, text-classification, Text Classification

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com search and recommendation system. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional machine learning product can be built from existing models.
Semi-supervised Time Series Classification by Temporal Relation Prediction
Authors: Haoyi Fan, Fengbin Zhang, Ruidong Wang, Xunhua Huang, and Zuoyong Li
Date: 2021-06-11 00:00:00
Proceeding: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 6
Tasks: Classification, Prediction, Relation, Relation Prediction, Semi-supervised time series classification, Time Series, Time Series Analysis, Time Series Classification

Semi-supervised learning (SSL) has proven to be a powerful algorithm in different domains by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data. However, few efforts consider the underlying temporal relation structure of unlabeled time series data in the semi-supervised learning paradigm. In this work, we propose a simple and effective method of Semi-supervised Time series classification architecture (termed as SemiTime) by gaining from the structure of unlabeled data in a self-supervised manner. Specifically, for the labeled time series, SemiTime conducts the supervised classification directly under the supervision of the annotated class label. For the unlabeled time series, the segments of past-future pair are sampled from time series, where two segments of pair from the same time series candidate are in positive temporal relation, while two segments from the different candidates are in negative temporal relation. Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner. Finally, by jointly classifying labeled data and predicting the temporal relation of unlabeled data, the useful representation of unlabeled time series can be captured by SemiTime. Extensive experiments on multiple real-world datasets show that SemiTime consistently outperforms the state-of-the-arts, which demonstrates the effectiveness of the proposed method.
MemCap: Memorizing Style Knowledge for Image Captioning
Authors: Wentian Zhao, Xinxiao wu, Xiaoxun Zhang
Date: 2020-04-03 00:00:00
Proceeding: AAAI 2020 4
Tasks: Image Captioning, Language Modeling, Language Modelling, Sentence

Generating stylized captions for images is a challenging task since it requires not only describing the content of the image accurately but also expressing the desired linguistic style appropriately. In this paper, we propose MemCap, a novel stylized image captioning method that explicitly encodes the knowledge about linguistic styles with memory mechanism. Rather than relying heavily on a language model to capture style factors in existing methods, our method resorts to memorizing stylized elements learned from training corpus. Particularly, we design a memory module that comprises a set of embedding vectors for encoding style-related phrases in training corpus. To acquire the style-related phrases, we develop a sentence decomposing algorithm that splits a stylized sentence into a style-related part that reflects the linguistic style and a content-related part that contains the visual content. When generating captions, our MemCap first extracts content-relevant style knowledge from the memory module via an attention mechanism and then incorporates the extracted knowledge into a language model. Extensive experiments on two stylized image captioning datasets (SentiCap and FlickrStyle10K) demonstrate the effectiveness of our method.
New approaches in agent-based modeling of complex financial systems
Authors: T. T. Chen, B. Zheng, Y. Li, X. F. Jiang
Date: 2017-03-04 00:00:00

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogenous personal preferences and interactions, these models are successful to explain the microscopic origination of the temporal and spatial correlations of the financial markets. We then present a novel paradigm combining the big-data analysis with the agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces, and develop an agent-based model to simulate the dynamic behaviors of the complex financial systems.
Artpedia
Authors: Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Massimiliano Corsini, and Rita Cucchiara
Date: 2019-09-01 00:00:00
Proceeding: International Conference on Image Analysis and Processing 2019 9
Tasks: Cross-Modal Retrieval, Retrieval

As vision and language techniques are widely applied to realistic images , there is a growing interest in designing visual-semantic models suitable for more complex and challenging scenarios. In this paper, we address the problem of cross-modal retrieval of images and sentences coming from the artistic domain. To this aim, we collect and manually annotate the Artpedia dataset that contains paintings and textual sentences describing both the visual content of the paintings and other contextual information. Thus, the problem is not only to match images and sentences, but also to identify which sentences actually describe the visual content of a given image. To this end, we devise a visual-semantic model that jointly addresses these two challenges by exploiting the latent alignment between visual and textual chunks. Experimental evaluations, obtained by comparing our model to different baselines, demonstrate the effectiveness of our solution and highlight the challenges of the proposed dataset. The Artpedia dataset is publicly available at: http://aimagelab.ing.unimore.it/artpedia.
OPEM (Open Source PEM Fuel Cell Simulation Tool)
Authors: Sepand Haghighi, Kasra Askari, Sarmin Hamidi, Mohammad Mahdi Rahimi
Date: 2018-07-22 00:00:00
Proceeding: The Journal of Open Source Software 2018 7
Tasks: Capacity Estimation, Density Estimation

Modeling and simulation of proton-exchange membrane fuel cells (PEMFC) may work as a powerful tool in the research & development of renewable energy sources. The Open-Source PEMFC Simulation Tool (OPEM) is a modeling tool for evaluating the performance of proton exchange membrane fuel cells. This package is a combination of models (static/dynamic) that predict the optimum operating parameters of PEMFC. OPEM contained generic models that will accept as input, not only values of the operating variables such as anode and cathode feed gas, pressure and compositions, cell temperature and current density, but also cell parameters including the active area and membrane thickness. In addition, some of the different models of PEMFC that have been proposed in the OPEM, just focus on one particular FC stack, and some others take into account a part or all auxiliaries such as reformers. OPEM is a platform for collaborative development of PEMFC models.
Python Random Graph Generator
Authors: Sepand Haghighi
Date: 2017-09-20 00:00:00
Proceeding: The Journal of Open Source Software 2017 9
Tasks: Benchmarking, Graph Generation

Pyrgg is an easy-to-use synthetic random graph generator written in Python which supports various graph file formats including DIMACS .gr files. Pyrgg has the ability to generate graphs of different sizes and is designed to provide input files for broad range of graph-based research applications, including but not limited to testing, benchmarking and performance-analysis of graph processing frameworks. Pyrgg target audiences are computer scientists who study graph algorithms and graph processing frameworks.
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework
Authors: Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Reza Hoseinnezhad
Date: 2021-08-27 00:00:00
Tasks: Anomaly Detection, Defect Detection, Few-Shot Learning, Transfer Learning

In this paper, we propose an efficient approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use \textit{global features} for feature extraction to summarize image content. However, global features are not robust against lighting and viewpoint changes and do not describe the image's geometrical information to be fully utilized in the manufacturing industry. To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrical information of the image regions. We model these local/point pattern features as a random finite set (RFS). In addition we propose RFS energy, in contrast to RFS likelihood as anomaly score. The similarity distribution of point pattern features of the normal sample has been modeled as a multivariate Gaussian. Parameters learning of the proposed RFS energy does not require any heavy computation. We evaluate the proposed approach on the MVTec AD dataset, a multi-object defect detection dataset. Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings.
A Partition Filter Network for Joint Entity and Relation Extraction
Authors: Zhiheng Yan, Chong Zhang, Jinlan Fu, Qi Zhang, Zhongyu Wei
Date: 2021-08-27 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction, Relation Prediction, Representation Learning

In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner, meaning that feature representation learning for each task is largely independent of each other except for input sharing. We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition. The shared partition represents inter-task information valuable to both tasks and is evenly shared across two tasks to ensure proper two-way interaction. The task partitions represent intra-task information and are formed through concerted efforts of both gates, making sure that encoding of task-specific features is dependent upon each other. Experiment results on six public datasets show that our model performs significantly better than previous approaches. In addition, contrary to what previous work has claimed, our auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way. The source code can be found at https://github.com/Coopercoppers/PFN.
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
Authors: Ling Chen, Jiahui Xu, Binqing Wu, Yuntao Qian, Zhenhong Du, Yansheng Li, Yongjun Zhang
Date: 2021-08-27 00:00:00
Tasks: graph construction, Graph Neural Network

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problem, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this paper, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on Chinese city air quality dataset indicate that our GAGNN outperforms existing forecasting models.
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data
Authors: Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Ali Karimoddini
Date: 2021-08-27 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation, Pedestrian Detection

This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
Authors: Hossein Rajaby Faghihi, Quan Guo, Andrzej Uszok, Aliakbar Nafar, Elaheh Raisi, Parisa Kordjamshidi
Date: 2021-08-27 00:00:00
Proceeding: EMNLP (ACL) 2021 11

We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the models' explainability in addition to the performance and generalizability in the low-data regime. Several approaches for such an integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such an integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such an integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(https://github.com/HLR/DomiKnowS).