pwc-archive/papers-with-abstracts huggingface dataset:
Institutional ownership and liquidity commonality: evidence from Australia
Authors: Reza Bradrania, Robert Elliott, Winston Wu
Date: 2022-11-07 00:00:00

We study the liquidity commonality impact of local and foreign institutional investment in the Australian equity market in the cross-section and over time. We find that commonality in liquidity is higher for large stocks compared to small stocks in the cross-section of stocks, and the spread between the two has increased over the past two decades. We show that this divergence can be explained by foreign institutional ownership. This finding suggests that foreign institutional investment contributes to an increase in the exposure of large stocks to unexpected liquidity events in the local market. We find a positive association between foreign institutional ownership and commonality in liquidity across all stocks, particularly in large and mid-cap stocks. Correlated trading by foreign institutions explains this association. However, local institutional ownership is positively related to the commonality in liquidity for large-cap stocks only.
Stressing Dynamic Loss Models
Authors: Emma Kroell, Silvana M. Pesenti, Sebastian Jaimungal
Date: 2022-11-06 00:00:00

Stress testing, and in particular, reverse stress testing, is a prominent exercise in risk management practice. Reverse stress testing, in contrast to (forward) stress testing, aims to find an alternative but plausible model such that under that alternative model, specific adverse stresses (i.e. constraints) are satisfied. Here, we propose a reverse stress testing framework for dynamic models. Specifically, we consider a compound Poisson process over a finite time horizon and stresses composed of expected values of functions applied to the process at the terminal time. We then define the stressed model as the probability measure under which the process satisfies the constraints and which minimizes the Kullback-Leibler divergence to the reference compound Poisson model. We solve this optimization problem, prove existence and uniqueness of the stressed probability measure, and provide a characterization of the Radon-Nikodym derivative from the reference model to the stressed model. We find that under the stressed measure, the intensity and the severity distribution of the process depend on time and state. We illustrate the dynamic stress testing by considering stresses on VaR and both VaR and CVaR jointly and provide illustrations of how the stochastic process is altered under these stresses. We generalize the framework to multivariate compound Poisson processes and stresses at times other than the terminal time. We illustrate the applicability of our framework by considering ``what if'' scenarios, where we answer the question: What is the severity of a stress on a portfolio component at an earlier time such that the aggregate portfolio exceeds a risk threshold at the terminal time? Furthermore, for general constraints, we propose an algorithm to simulate sample paths under the stressed measure, thus allowing to compare the effects of stresses on the dynamics of the process.
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Authors: Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
Date: 2022-11-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic pipeline that collects dynamic datasets from real-world markets and processes them into gym-style market environments. Second, we reproduce popular papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: https://github.com/AI4Finance-Foundation/FinRL-Meta
Changes from Classical Statistics to Modern Statistics and Data Science
Authors: Kai Zhang, Shan Liu, Momiao Xiong
Date: 2022-10-30 00:00:00

A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical formulas, social networks, transportation and sensor networks, computer visions, automations, and biomedical measurements. The Euclidean assumption is not appropriate for non Euclidean data. This perspective addresses the urgent need to overcome those fundamental limitations and encourages extensions of classical probability theory and hypothesis testing , diffusion models and stochastic differential equations from Euclidean space to non Euclidean space. Artificial intelligence such as natural language processing, computer vision, graphical neural networks, manifold regression and inference theory, manifold learning, graph neural networks, compositional diffusion models for automatically compositional generations of concepts and demystifying machine learning systems, has been rapidly developed. Differential manifold theory is the mathematic foundations of deep learning and data science as well. We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets. A general framework for integrated analysis of both Euclidean and non Euclidean data, composite AI, decision intelligence and edge AI provide powerful innovative ideas and strategies for fundamentally advancing AI. We are expected to marry statistics with AI, develop a unified theory of modern statistics and drive next generation of AI and data science.
Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer
Authors: Shikhar Nilabh, Fidel Grandia
Date: 2022-10-25 00:00:00

Numerous polluted groundwater sites across the globe require an active remediation strategy to restore natural environmental conditions and local ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on the laboratory scale, their application in real field case conditions is still limited. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. Therefore, a predictive tool for understanding the transport and retention behavior of ENPs is highly required. The existing tools in the literature are dominated with numerical simulators, which have limited flexibility and accuracy in the presence of sparse datasets. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer. The result from the forward model demonstrates the effective capability of dw-PINN in accurately predicting the ENPs mobility. The model verification step shows that the relative mean square error (MSE) of the predicted ENPs concentration using dw-PINN converges to a minimum value of $1.3{e^{-5}}$. In the subsequent step, the result from the inverse model estimates the governing parameters of ENPs mobility with reasonable accuracy. The research demonstrates the tool's capability to provide predictive insights for developing an efficient groundwater remediation strategy.
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
Authors: Leonie Basso, Zhao Ren, Wolfgang Nejdl
Date: 2022-10-27 00:00:00
Tasks: Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. The use of wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Although deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity. In this work, we propose to use lightweight convolutional neural networks (CNNs) with parameterised hypercomplex (PH) layers for AF detection based on ECGs. The proposed approach trains small-scale CNNs, thus overcoming the limited computing resources on wearable devices. We show comparable performance to corresponding real-valued CNNs on two publicly available ECG datasets using significantly fewer model parameters. PH models are more flexible than other hypercomplex neural networks and can operate on any number of input ECG leads.
Automatic Seizure Prediction using CNN and LSTM
Authors: Abhijeet Bhattacharya
Date: 2022-10-28 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Feature Engineering, Prediction, Seizure prediction

The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the doctors and clinical experts and reduces their workload. This paper proposes an end-to-end deep learning algorithm to fully automate seizure prediction's laborious task without any heavy pre-processing on the EEG data or feature engineering. The proposed deep learning network is a blend of signal processing and deep learning pipeline, which automates the seizure prediction framework using the EEG signals. This proposed model was evaluated on an open EEG dataset, CHB-MIT. The network achieved an average sensitivity of 97.746\text{\%} and a false positive rate (FPR) of 0.2373 per hour.
Knowledge Retrieval
Authors: Vishnu Vardhan Reddy Palli
Date: 2022-10-22 00:00:00
Tasks: Retrieval

Robots are man made machines which are used to accomplish the tasks. Robots are mainly used to do complex tasks and work in hazardous environment where humans are difficult to work. They are not only designed to use in hazardous environment but also in the environment where humans are performing the same task repeatedly. These are also used for cooking purpose some tasks can be completed with the interaction of both the human and robot one of such things is cooking where human should help robot in making dishes. This paper mainly focusses on Functional Object Oriented Network which is structured knowledge representation using the input output and motion nodes. Task tress are generated using the task tree FOON is produced and collections of all FOONS forms the universal FOON. Different algorithms to traverse the tree in order to get the best output are also discussed in this paper. The desired node or goal node can be achieved from the start node using the different search algorithms and comparison between them is discussed.
Selective Sub-DODAGs Hiding" SSDH" a new Attack in IoT RPL-Based Networks
Authors: Sid Ahmed Hichame Belkhiraa, Mehdi Rouissatb Mohammed Belkheirc, Merahi Bouzianid
Date: 2022-10-12 00:00:00
Proceeding: Turkish Journal of Computer and Mathematics Education 2022 10

The security concern in RPL-based IoT networks has gained a close attention by the researchers in the recent past 
years. This is due to the inherent constraints of nodes and the frequent change in the network topology. This makes the network 
vulnerable to various attacks that intend to disrupt the network availability and nodes resources. Heavy and decentralized 
algorithms seeking to enable the security in RPL networks as cryptographic schemes or expensive solutions are not convenient 
due to intrinsic characteristics of IoT nodes. In this paper, we propose and analyse a new internal hybrid routing attack against 
RPL, named SSDH for Selective Sub-DODAGs Hiding attack. The intruder node performing the present attack intends to 
isolate and exhaust the resources of a targeted subset of nodes by a selective hiding, based on its routing table. Compared to 
other known isolation attacks, the proposed one shows a silent malicious behavior, in which the root never knows about the 
isolated nodes. After the selective hiding process by dropping all packets coming from its direct or indirect children nodes, the 
intruder node follows its malicious behavior by provoking the victim nodes to conduct a DIO flooding based on falsified DIO 
messages. Simulation results obviously depict the harmful disruption of the present attack in different scenarios of RPL-based 
networks, by measuring the main QoS metrics: Control overhead, energy consumed, packet delivery ration and average delay. 
It should be noticed that the present paper describes the process of the new proposed at- tack and its effect, where the 
preventive and mitigation solutions will be the subject of a future work
Significance-Based Categorical Data Clustering
Authors: Lianyu Hu, Mudi Jiang, Yan Liu, Zengyou He
Date: 2022-11-08 00:00:00
Tasks: Categorical data clustering, Clustering

Although numerous algorithms have been proposed to solve the categorical data clustering problem, how to access the statistical significance of a set of categorical clusters remains unaddressed. To fulfill this void, we employ the likelihood ratio test to derive a test statistic that can serve as a significance-based objective function in categorical data clustering. Consequently, a new clustering algorithm is proposed in which the significance-based objective function is optimized via a Monte Carlo search procedure. As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number. Extensive experimental studies suggest that our method is able to achieve comparable performance to state-of-the-art categorical data clustering algorithms. Moreover, the effectiveness of such a significance-based formulation on statistical cluster validation and cluster number estimation is demonstrated through comprehensive empirical results.
Review of coreference resolution in English and Persian
Authors: Hassan Haji Mohammadi, Alireza Talebpour, Ahmad Mahmoudi Aznaveh, Samaneh Yazdani
Date: 2022-11-08 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Document Summarization, Graph Neural Network, Machine Translation, Translation

Coreference resolution (CR), identifying expressions referring to the same real-world entity, is a fundamental challenge in natural language processing (NLP). This paper explores the latest advancements in CR, spanning coreference and anaphora resolution. We critically analyze the diverse corpora that have fueled CR research, highlighting their strengths, limitations, and suitability for various tasks. We examine the spectrum of evaluation metrics used to assess CR systems, emphasizing their advantages, disadvantages, and the need for more nuanced, task-specific metrics. Tracing the evolution of CR algorithms, we provide a detailed overview of methodologies, from rule-based approaches to cutting-edge deep learning architectures. We delve into mention-pair, entity-based, cluster-ranking, sequence-to-sequence, and graph neural network models, elucidating their theoretical foundations and performance on benchmark datasets. Recognizing the unique challenges of Persian CR, we dedicate a focused analysis to this under-resourced language. We examine existing Persian CR systems and highlight the emergence of end-to-end neural models leveraging pre-trained language models like ParsBERT. This review is an essential resource for researchers and practitioners, offering a comprehensive overview of the current state-of-the-art in CR, identifying key challenges, and charting a course for future research in this rapidly evolving field.
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Authors: Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Date: 2022-11-08 00:00:00
Tasks: Causal Inference, Program Synthesis

Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network (NN) architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER), a generalized method for causal effect estimation. NESTER integrates the ideas used in existing methods based on multi-head NNs for causal effect estimation into one framework. We design a Domain Specific Language (DSL) tailored for causal effect estimation based on causal inductive biases used in literature. We conduct a theoretical analysis to investigate NESTER's efficacy in estimating causal effects. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.
When & How to Transfer with Transfer Learning
Authors: Adrian Tormos, Dario Garcia-Gasulla, Victor Gimenez-Abalos, Sergio Alvarez-Napagao
Date: 2022-11-08 00:00:00
Tasks: Transfer Learning

In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks. Visual features learnt for one task have been shown to be reusable for other tasks, improving performance significantly. By reusing deep representations, TL enables the use of deep models in domains with limited data availability, limited computational resources and/or limited access to human experts. Domains which include the vast majority of real-life applications. This paper conducts an experimental evaluation of TL, exploring its trade-offs with respect to performance, environmental footprint, human hours and computational requirements. Results highlight the cases were a cheap feature extraction approach is preferable, and the situations where an expensive fine-tuning effort may be worth the added cost. Finally, a set of guidelines on the use of TL are proposed.
An Ensemble-based approach for assigning text to correct Harmonized system code
Authors: Shubham, Avinash Arya, Subarna Roy, Sridhar Jonnala
Date: 2022-11-08 00:00:00
Tasks: Knowledge Graphs, NER

Industries must follow government rules and regulations around the world to classify products when assessing duties and taxes for international shipment. Harmonized System (HS) is the most standardized numerical method of classifying traded products among industry classification systems. A hierarchical ensemble model comprising of Bert- transformer, NER, distance-based approaches, and knowledge-graphs have been developed to address scalability, coverage, ability to capture nuances, automation and auditing requirements when classifying unknown text-descriptions as per HS method.
From Causal Pairs to Causal Graphs
Authors: Rezaur Rashid, Jawad Chowdhury, Gabriel Terejanu
Date: 2022-11-08 00:00:00

Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a different approach and generate a probability distribution over all possible graphs informed by the cause-effect pair features proposed in response to the workshop challenge. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches. Our experiments, on both synthetic and real datasets, show that our proposed methods not only have statistically similar or better performances than some traditional approaches but also are computationally faster.
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Authors: Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, Noseong Park, Sung-Bae Cho
Date: 2022-11-08 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Time Series, Time Series Analysis, Time Series Forecasting

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Authors: Yangning Li, Yinghui Li, Xi Chen, Hai-Tao Zheng, Ying Shen, Hong-Gee Kim
Date: 2022-11-08 00:00:00
Tasks: Active Learning, Outlier Detection, Relation, Relation Extraction

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.