pwc-archive/papers-with-abstracts huggingface dataset:
From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track
Authors: Weronika Lajewska, Krisztian Balog
Date: 2023-01-25 00:00:00
Tasks: Retrieval

This paper reports on an effort of reproducing the organizers' baseline as well as the top performing participant submission at the 2021 edition of the TREC Conversational Assistance track. TREC systems are commonly regarded as reference points for effectiveness comparison. Yet, the papers accompanying them have less strict requirements than peer-reviewed publications, which can make reproducibility challenging. Our results indicate that key practical information is indeed missing. While the results can be reproduced within a 19% relative margin with respect to the main evaluation measure, the relative difference between the baseline and the top performing approach shrinks from the reported 18% to 5%. Additionally, we report on a new set of experiments aimed at understanding the impact of various pipeline components. We show that end-to-end system performance can indeed benefit from advanced retrieval techniques in either stage of a two-stage retrieval pipeline. We also measure the impact of the dataset used for fine-tuning the query rewriter and find that employing different query rewriting methods in different stages of the retrieval pipeline might be beneficial. Moreover, these results are shown to generalize across the 2020 and 2021 editions of the track. We conclude our study with a list of lessons learned and practical suggestions.
Counterfactual Editing for Search Result Explanation
Authors: Zhichao Xu, Hemank Lamba, Qingyao Ai, Joel Tetreault, Alex Jaimes
Date: 2023-01-25 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Retrieval

Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factual explanation, i.e. to find/generate supporting evidence about documents' relevance to search queries. However, research in cognitive sciences has shown that human explanations are contrastive i.e. people explain an observed event using some counterfactual events; such explanations reduce cognitive load and provide actionable insights. Though already proven effective in machine learning and NLP communities, there lacks a strict formulation on how counterfactual explanations should be defined and structured, in the context of web search. In this paper, we first discuss the possible formulation of counterfactual explanations in the IR context. Next, we formulate a suite of desiderata for counterfactual explanation in SeRE task and corresponding automatic metrics. With this desiderata, we propose a method named \textbf{C}ounter\textbf{F}actual \textbf{E}diting for Search Research \textbf{E}xplanation (\textbf{CFE2}). CFE2 provides pairwise counterfactual explanations for document pairs within a search engine result page. Our experiments on five public search datasets demonstrate that CFE2 can significantly outperform baselines in both automatic metrics and human evaluations.
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
Authors: Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata
Date: 2023-01-25 00:00:00
Proceeding: CAAI Transactions on Intelligence Technology 2023 1
Tasks: feature selection, Inductive Bias, Node Classification

Graphs help to define the relationships between entities in the data. These relationships, represented by edges, often provide additional context information which can be utilised to discover patterns in the data. Graph Neural Networks (GNNs) employ the inductive bias of the graph structure to learn and predict on various tasks. The primary operation of graph neural networks is the feature aggregation step performed over neighbours of the node based on the structure of the graph. In addition to its own features, for each hop, the node gets additional combined features from its neighbours. These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification. However, in real-world data, features of neighbours at different hops may not correlate with the node's features. Thus, any indiscriminate feature aggregation by GNN might cause the addition of noisy features leading to degradation in model's performance. In this work, we show that selective aggregation of node features from various hops leads to better performance than default aggregation on the node classification task. Furthermore, we propose a Dual-Net GNN architecture with a classifier model and a selector model. The classifier model trains over a subset of input node features to predict node labels while the selector model learns to provide optimal input subset to the classifier for the best performance. These two models are trained jointly to learn the best subset of features that give higher accuracy in node label predictions. With extensive experiments, we show that our proposed model outperforms both feature selection methods and state-of-the-art GNN models with remarkable improvements up to 27.8%.
Proxy Graph Matching with Proximal Matching Networks
Authors: Haoru Tan, Chuang Wang, Sitong Wu, Tie-Qiang Wang, Xu-Yao Zhang, Cheng-Lin Liu
Date: 2021-10-16 00:00:00
Proceeding: AAAI 2021 10
Tasks: Graph Matching, Graph Neural Network

Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Moreover, we find most methods are sensitive to global transforms, for example, a random rotation. On the contrary, classical geometric approaches are immune to rotational transformation though their performance is generally inferior. To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. The model only takes geometric information as input. It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization. To justify our approach, we provide a convergence guarantee for the proximal method for graph matching. The overall performance is validated by numerical experiments. In particular, our approach is trained on the synthetic random graphs and then applied to several real-world datasets. The experimental results demonstrate that our method is robust to rotational transform and highlights its strong performance of matching accuracy.
Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
Authors: Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng, Yanzhao Lai, Jimin Huang
Date: 2023-01-25 00:00:00
Tasks: Hierarchical Reinforcement Learning, PAIR TRADING, reinforcement-learning, Reinforcement Learning (RL)

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.
Consensus Algorithm For Calculation Of Protein Binding Affinity Using Multiple Models
Authors: Ayşenaz Ezgi Ergin, Deniz Turgay Altılar
Date: 2023-01-25 00:00:00

The major histocompatibility complex (MHC) molecules, which bind peptides for presentation on the cell surface, play an important role in cell-mediated immunity. In light of developing databases and technologies over the years, significant progress has been made in research on peptide binding affinity calculation. Several in techniques have been developed to predict peptide binding to MHC class I. Most of the research on MHC Class I due to its nature brings better performance and more. Considering the use of different methods and different technologies, and the approach of similar methods on different proteins, a classification was created according to the binding affinity of protein peptides.
Educational Game on Cryptocurrency Investment: Using Microeconomic Decision Making to Understand Macroeconomics Principles
Authors: Jiasheng Zhu, Luyao Zhang
Date: 2023-01-25 00:00:00
Tasks: Decision Making, Management, Marketing

Gamification is an effective strategy for motivating and engaging users, which is grounded in business, marketing, and management by designing games in nongame contexts. Gamifying education, which consists of the design and study of educational games, is an emerging trend. However, the existing classroom games for understanding macroeconomics have weak connections to the microfoundations of individual decision-making. We design an educational game on cryptocurrency investment for understanding macroeconomic concepts in microeconomic decisions. We contribute to the literature by designing game-based learning that engages students in understanding macroeconomics in incentivized individual investment decisions. Our game can be widely implemented in online, in-person, and hybrid classrooms. We also reflect on strategies for improving the user experience for future educational game implementations.
Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
Authors: Siqi Zhang, Na Yi, Yi Ma
Date: 2023-01-25 00:00:00

Consider robot swarm wireless networks where mobile robots offload their computing tasks to a computing server located at the mobile edge. Our aim is to maximize the swarm lifetime through efficient exploitation of the correlation between distributed data sources. The optimization problem is handled by selecting appropriate robot subsets to send their sensed data to the server. In this work, the data correlation between distributed robot subsets is modelled as an undirected graph. A least-degree iterative partitioning (LDIP) algorithm is proposed to partition the graph into a set of subgraphs. Each subgraph has at least one vertex (i.e., subset), termed representative vertex (R-Vertex), which shares edges with and only with all other vertices within the subgraph; only R-Vertices are selected for data transmissions. When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel. For independent fading channels, the max-min principle can be incorporated into the proposed approach to achieve the best performance.
Downlink TDMA Scheduling for IRS-aided Communications with Block-Static Constraints
Authors: Alberto Rech, Matteo Pagin, Stefano Tomasin, Federico Moretto, Leonardo Badia, Marco Giordani, Jonathan Gambini, Michele Zorzi
Date: 2023-01-25 00:00:00
Tasks: Scheduling

Intelligent reflecting surfaces (IRSs) are being studied as possible low-cost energy-efficient alternatives to active relays, with the goal of solving the coverage issues of millimeter wave (mmWave) and terahertz (THz) network deployments. In the literature, these surfaces are often studied by idealizing their characteristics. Notably, it is often assumed that IRSs can tune with arbitrary frequency the phase-shifts induced by their elements, thanks to a wire-like control channel to the next generation node base (gNB). Instead, in this work we investigate an IRS-aided time division multiple access (TDMA) cellular network, where the reconfiguration of the IRS may entail an energy or communication cost, and we aim at limiting the number of reconfigurations over time. We develop a clustering-based heuristic scheduling, which optimizes the system sum-rate subject to a given number of reconfigurations within the TDMA frame. To such end, we first cluster user equipments (UEs) with a similar optimal IRS configuration. Then, we compute an overall IRS cluster configuration, which can be thus kept constant while scheduling the whole UEs cluster. Numerical results show that our approach is effective in supporting IRSs-aided systems with practical constraints, achieving up to 85% of the throughput obtained by an ideal deployment, while providing a 50% reduction in the number of IRS reconfigurations.
Lost in Algorithms
Authors: Andrew N. Sloss
Date: 2023-01-02 00:00:00

Algorithms are becoming more capable, and with that comes hic sunt dracones (here be dragons). The term symbolizes areas beyond our known maps. We use this term since we are stepping into an exciting, potentially dangerous, and unknown area with algorithms. Our curiosity to understand the natural world drives our search for new methods. For this reason, it is crucial to explore this subject. The project's objective is to overlay the information obtained, in conjunction with the state of hardware today, to see if we can determine the likely directions for future algorithms'. Even though we slightly cover non-classical computing in this paper, our primary focus is on classical computing (i.e., digital computers). It is worth noting that non-classical quantum computing requires classical computers to operate; they are not mutually exclusive.
Object Segmentation with Audio Context
Authors: Kaihui Zheng, Yuqing Ren, Zixin Shen, Tianxu Qin
Date: 2023-01-04 00:00:00
Tasks: audio-visual learning, Decoder, Instance Segmentation, Object, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Segmentation, Video Semantic Segmentation

Visual objects often have acoustic signatures that are naturally synchronized with them in audio-bearing video recordings. For this project, we explore the multimodal feature aggregation for video instance segmentation task, in which we integrate audio features into our video segmentation model to conduct an audio-visual learning scheme. Our method is based on existing video instance segmentation method which leverages rich contextual information across video frames. Since this is the first attempt to investigate the audio-visual instance segmentation, a novel dataset, including 20 vocal classes with synchronized video and audio recordings, is collected. By utilizing combined decoder to fuse both video and audio features, our model shows a slight improvements compared to the base model. Additionally, we managed to show the effectiveness of different modules by conducting extensive ablations.
The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery
Authors: Kishore Vasan, Deisy Gysi, Albert-Laszlo Barabasi
Date: 2023-01-25 00:00:00
Tasks: Drug Discovery

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome.
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles
Authors: Martin Higgins, Bruce Stephen, David Wallom
Date: 2023-01-25 00:00:00
Tasks: Anomaly Detection, Clustering

Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. In this paper, we consider attacks which aim to alter the consumer load profile to exploit intraday price dips. We examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. In this way we outline a methodology for detecting attacks on industrial load smart meters. We first create a feature clustering model of the underlying business, segregated by business type. We then use these clusters to create an incentive-weighted anomaly detection protocol for false data attacks against load profiles. This clustering-based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, we model incentive-based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.
A Positioning System in an Urban Vertical Heterogeneous Network (VHetNet)
Authors: Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu
Date: 2023-01-24 00:00:00

Global navigation satellite systems (GNSSs) are essential in providing localization and navigation services to most of the world due to their superior coverage. However, due to high pathloss and inevitable atmospheric effect, the positioning performance of any standalone GNSS is still poor in urban areas. To improve the positioning performance of legacy GNSSs in urban areas, a positioning system, which utilizes high altitude platform station (HAPS) and 5G gNodeBs (gNBs), in a futuristic urban vertical heterogeneous network (VHetNet) is proposed. In this paper, we demonstrate the effectiveness of gNBs in improving the vertical positioning accuracy for both the GPS-only system and the HAPS-aided GPS system by analyzing the impact of the density of gNBs and the pseudorange error of gNB on the positioning performance of the gNB augmented positioning systems. We also demonstrate the effectiveness of receiver autonomous integrity monitoring (RAIM) algorithms on the HAPS and/or gNB aided GPS systems in urban areas.
Automatic Debiased Estimation with Machine Learning-Generated Regressors
Authors: Juan Carlos Escanciano, Telmo Pérez-Izquierdo
Date: 2023-01-25 00:00:00
Tasks: counterfactual, Dimensionality Reduction, Feature Engineering, Imputation

Many parameters of interest in economics and other social sciences depend on generated regressors. Examples in economics include structural parameters in models with endogenous variables estimated by control functions and in models with sample selection, treatment effect estimation with propensity score matching, and marginal treatment effects. More recently, Machine Learning (ML) generated regressors are becoming ubiquitous for these and other applications such as imputation with missing regressors, dimension reduction, including autoencoders, learned proxies, confounders and treatments, and for feature engineering with unstructured data, among others. We provide the first general method for valid inference with regressors generated from ML. Inference with generated regressors is complicated by the very complex expression for influence functions and asymptotic variances. Additionally, ML-generated regressors may lead to large biases in downstream inferences. To address these problems, we propose Automatic Locally Robust/debiased GMM estimators in a general three-step setting with ML-generated regressors. We illustrate our results with treatment effects and counterfactual parameters in the partially linear and nonparametric models with ML-generated regressors. We provide sufficient conditions for the asymptotic normality of our debiased GMM estimators and investigate their finite-sample performance through Monte Carlo simulations.
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Authors: Alain Hecq, Marie Ternes, Ines Wilms
Date: 2023-01-25 00:00:00
Tasks: Demand Forecasting

Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reduce the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on two empirical applications, one on realized volatility forecasting with macroeconomic data and another on demand forecasting for a bicycle-sharing system with ridership data on other transportation types.
Pain or Anxiety? The Health Consequences of Rising Robot Adoption in China
Authors: Qiren Liu, Sen Luo, Robert Seamans
Date: 2023-01-25 00:00:00
Tasks: Industrial Robots

The rising adoption of industrial robots is radically changing the role of workers in the production process. Robots can be used for some of the more physically demanding and dangerous production work, thus reducing the possibility of worker injury. On the other hand, robots may replace workers, potentially increasing worker anxiety about their job safety. In this paper, we investigate how individual physical health and mental health outcomes vary with local exposure to robots for manufacturing workers in China. We find a link between robot exposure and better physical health of workers, particularly for younger workers and those with less education. However, we also find that robot exposure is associated with more mental stress for Chinese workers, particularly older and less educated workers.