pwc-archive/papers-with-abstracts huggingface dataset:
Minimax Rates for High-Dimensional Random Tessellation Forests
Authors: Eliza O'Reilly, Ngoc Mai Tran
Date: 2021-09-22 00:00:00
Tasks: Learning Theory, Vocal Bursts Intensity Prediction

Random forests are a popular class of algorithms used for regression and classification. The algorithm introduced by Breiman in 2001 and many of its variants are ensembles of randomized decision trees built from axis-aligned partitions of the feature space. One such variant, called Mondrian forests, was proposed to handle the online setting and is the first class of random forests for which minimax rates were obtained in arbitrary dimension. However, the restriction to axis-aligned splits fails to capture dependencies between features, and random forests that use oblique splits have shown improved empirical performance for many tasks. In this work, we show that a large class of random forests with general split directions also achieve minimax optimal convergence rates in arbitrary dimension. This class includes STIT forests, a generalization of Mondrian forests to arbitrary split directions, as well as random forests derived from Poisson hyperplane tessellations. These are the first results showing that random forest variants with oblique splits can obtain minimax optimality in arbitrary dimension. Our proof technique relies on the novel application of the theory of stationary random tessellations in stochastic geometry to statistical learning theory.
Investigating Entropy for Extractive Document Summarization
Authors: Alka Khurana, Vasudha Bhatnagar
Date: 2021-09-22 00:00:00
Tasks: Attribute, Document Summarization, Extractive Document Summarization, Informativeness, Semantic Similarity, Semantic Textual Similarity, Text Summarization

Automatic text summarization aims to cut down readers time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document summary generated by an algorithm, and selecting sentences that capture the essence of a document is the primary goal of extractive document summarization. In this paper, we employ Shannon entropy to capture informativeness of sentences. We employ Non-negative Matrix Factorization (NMF) to reveal probability distributions for computing entropy of terms, topics, and sentences in latent space. We present an information theoretic interpretation of the computed entropy, which is the bedrock of the proposed E-Summ algorithm, an unsupervised method for extractive document summarization. The algorithm systematically applies information theoretic principle for selecting informative sentences from important topics in the document. The proposed algorithm is generic and fast, and hence amenable to use for summarization of documents in real time. Furthermore, it is domain-, collection-independent and agnostic to the language of the document. Benefiting from strictly positive NMF factor matrices, E-Summ algorithm is transparent and explainable too. We use standard ROUGE toolkit for performance evaluation of the proposed method on four well known public data-sets. We also perform quantitative assessment of E-Summ summary quality by computing its semantic similarity w.r.t the original document. Our investigation reveals that though using NMF and information theoretic approach for document summarization promises efficient, explainable, and language independent text summarization, it needs to be bolstered to match the performance of deep neural methods.
Astronomical Pipeline Provenance: A Use Case Evaluation
Authors: Michael A. C. Johnson, Marcus Paradies, Marta Dembska, Kristen Lackeos, Hans-Rainer Klöckner, David J. Champion, Sirko Schindler
Date: 2021-09-22 00:00:00
Tasks: Astronomy

In this decade astronomy is undergoing a paradigm shift to handle data from next generation observatories such as the Square Kilometre Array (SKA) or the Vera C. Rubin Observatory (LSST). Producing real time data streams of up to 10 TB/s and data products of the order of 600 Pbytes/year, the SKA will be the biggest civil data producing machine of the world that demands novel solutions on how these data volumes can be stored and analysed. Through the use of complex, automated pipelines the provenance of this real time data processing is key to establish confidence within the system, its final data products, and ultimately its scientific results. The intention of this paper is to lay the foundation for making an automated provenance generation tool for astronomical/data-processing pipelines. We therefore present a use case analysis, specific to the astronomical needs which addresses the issues of trust and reproducibility as well as other ulterior use cases which are of interest to astronomers. This analysis is subsequently used as the basis to discuss the requirements, challenges, and opportunities involved in designing both the tool and the associated provenance model.
Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection
Authors: Negar Arabzadeh, Xinyi Yan, Charles L. A. Clarke
Date: 2021-09-22 00:00:00
Tasks: GPU, Information Retrieval, Retrieval

Over the last few years, contextualized pre-trained transformer models such as BERT have provided substantial improvements on information retrieval tasks. Recent approaches based on pre-trained transformer models such as BERT, fine-tune dense low-dimensional contextualized representations of queries and documents in embedding space. While these dense retrievers enjoy substantial retrieval effectiveness improvements compared to sparse retrievers, they are computationally intensive, requiring substantial GPU resources, and dense retrievers are known to be more expensive from both time and resource perspectives. In addition, sparse retrievers have been shown to retrieve complementary information with respect to dense retrievers, leading to proposals for hybrid retrievers. These hybrid retrievers leverage low-cost, exact-matching based sparse retrievers along with dense retrievers to bridge the semantic gaps between query and documents. In this work, we address this trade-off between the cost and utility of sparse vs dense retrievers by proposing a classifier to select a suitable retrieval strategy (i.e., sparse vs. dense vs. hybrid) for individual queries. Leveraging sparse retrievers for queries which can be answered with sparse retrievers decreases the number of calls to GPUs. Consequently, while utility is maintained, query latency decreases. Although we use less computational resources and spend less time, we still achieve improved performance. Our classifier can select between sparse and dense retrieval strategies based on the query alone. We conduct experiments on the MS MARCO passage dataset demonstrating an improved range of efficiency/effectiveness trade-offs between purely sparse, purely dense or hybrid retrieval strategies, allowing an appropriate strategy to be selected based on a target latency and resource budget.
A Survey on Reinforcement Learning for Recommender Systems
Authors: Yuanguo Lin, Yong liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
Date: 2021-09-22 00:00:00
Tasks: Explainable Recommendation, Interactive Recommendation, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Recommendation, Survey

Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years, owing to the interactive nature and autonomous learning ability. Empirical results show that RL-based recommendation methods often surpass most of supervised learning methods. Nevertheless, there are various challenges of applying RL in recommender systems. To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems. To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational recommendatin, sequential recommendation, and explainable recommendation. Furthermore, we systematically analyze the challenges and relevant solutions on the basis of existing literature. Finally, under discussion for open issues of RL and its limitations of recommender systems, we highlight some potential research directions in this field.
Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
Authors: Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma
Date: 2021-09-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Web search heavily relies on click-through behavior as an essential feedback signal for performance improvement and evaluation. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click (especially non-click after examination) is regarded as a signal of irrelevance or uselessness. However, there are many cases where users do not click on any search results but still satisfy their information need with the contents of the results shown on the Search Engine Result Page (SERP). This raises the problem of measuring result usefulness and modeling user satisfaction in "Zero-click" search scenarios. Previous works have solved this issue by (1) detecting user satisfaction for abandoned SERP with context information and (2) considering result-level click necessity with external assessors' annotations. However, few works have investigated the reason behind non-click behavior and estimated the usefulness of non-click results. A challenge for this research question is how to collect valuable feedback for non-click results. With neuroimaging technologies, we design a lab-based user study and reveal differences in brain signals while examining non-click search results with different usefulness levels. The findings in significant brain regions and electroencephalogram~(EEG) spectrum also suggest that the process of usefulness judgment might involve similar cognitive functions of relevance perception and satisfaction decoding. Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i.e., content and context factors). Results show that it is feasible to utilize brain signals to improve usefulness estimation performance and enhancing human-computer interactions in "Zero-click" search scenarios.
Fairwalk: Towards fair graph embedding
Authors: Tahleen Rahman, Bartlomiej Surma, Michael Backes and Yang Zhang
Date: 2019-01-01 00:00:00
Proceeding: IJCAI 2019 1
Tasks: Fairness, Graph Embedding

Graph embeddings have gained huge popularity in
the recent years as a powerful tool to analyze social networks. However, no prior works have studied potential bias issues inherent within graph embedding. In this paper, we make a first attempt in
this direction. In particular, we concentrate on the
fairness of node2vec, a popular graph embedding
method. Our analyses on two real-world datasets
demonstrate the existence of bias in node2vec when
used for friendship recommendation. We therefore propose a fairness-aware embedding method,
namely Fairwalk, which extends node2vec. Experimental results demonstrate that Fairwalk reduces
bias under multiple fairness metrics while still preserving the utility
Input-Output History Feedback Controller for Encrypted Control with Leveled Fully Homomorphic Encryption
Authors: Kaoru Teranishi, Tomonori Sadamoto, Kiminao Kogiso
Date: 2021-09-22 00:00:00

Protecting the parameters, states, and input/output signals of a dynamic controller is essential for securely outsourcing its computation to an untrusted third party. Although a fully homomorphic encryption scheme allows the evaluation of controller operations with encrypted data, an encrypted dynamic controller with the encryption scheme destabilizes a closed-loop system or degrades the control performance due to overflow. This paper presents a novel controller representation based on input-output history data to implement an encrypted dynamic controller that operates without destabilization and performance degradation. Implementation of this encrypted dynamic controller representation can be optimized via batching techniques to reduce the time and space complexities. Furthermore, this study analyzes the stability and performance degradation of a closed-loop system caused by the effects of controller encryption. A numerical simulation demonstrates the feasibility of the proposed encrypted control scheme, which inherits the control performance of the original controller at a sufficient level.
Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data
Authors: Suphanut Jamonnak, Ye Zhao, Xinyi Huang, Md Amiruzzaman
Date: 2021-08-20 00:00:00
Tasks: Autonomous Driving

Vision-based deep learning (DL) methods have made great progress in learning autonomous driving models from large-scale crowd-sourced video datasets. They are trained to predict instantaneous driving behaviors from video data captured by on-vehicle cameras. In this paper, we develop a geo-context aware visualization system for the study of Autonomous Driving Model (ADM) predictions together with large-scale ADM video data. The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques. Model performance measures can be studied together with a set of geospatial attributes over map views. Users can also discover and compare prediction behaviors of multiple DL models in both city-wide and street-level analysis, together with road images and video contents. Therefore, the system provides a new visual exploration platform for DL model designers in autonomous driving. Use cases and domain expert evaluation show the utility and effectiveness of the visualization system.
On the Comparison of Single-Carrier vs. Digital Multi-Carrier Signaling for Long-Haul Transmission of Probabilistically Shaped Constellation Formats
Authors: Kaoutar Benyahya, Amirhossein Ghazisaeidi, Vahid Aref, Mathieu Chagnon, Aymeric Arnould, Stenio Ranzini, Haik Mardoyan, Fred Buchali, Jeremie Renaudier
Date: 2021-09-22 00:00:00

We report on theoretical and experimental investigations of the nonlinear tolerance of single carrier and digital multicarrier approaches with probabilistically shaped constellations. Experimental transmission of PCS16QAM is assessed at 120 GBd over an ultra-long-haul distance.
Sensor-Based Satellite IoT for Early Wildfire Detection
Authors: How-Hang Liu, Ronald Y. Chang, Yi-Ying Chen, I-Kang Fu
Date: 2021-09-22 00:00:00

Frequent and severe wildfires have been observed lately on a global scale. Wildfires not only threaten lives and properties, but also pose negative environmental impacts that transcend national boundaries (e.g., greenhouse gas emission and global warming). Thus, early wildfire detection with timely feedback is much needed. We propose to use the emerging beyond fifth-generation (B5G) and sixth-generation (6G) satellite Internet of Things (IoT) communication technology to enable massive sensor deployment for wildfire detection. We propose wildfire and carbon emission models that take into account real environmental data including wind speed, soil wetness, and biomass, to simulate the fire spreading process and quantify the fire burning areas, carbon emissions, and economical benefits of the proposed system against the backdrop of recent California wildfires. We also conduct a satellite IoT feasibility check by analyzing the satellite link budget. Future research directions to further illustrate the promise of the proposed system are discussed.
Reliable Linearized Phase Retrieval for Near-Field Antenna Measurements with Truncated Measurement Surfaces
Authors: Alexander Paulus, Josef Knapp, Jonas Kornprobst, Thomas F. Eibert
Date: 2021-09-20 00:00:00
Tasks: Retrieval

Most methods tackling the phase retrieval problem of magnitude-only antenna measurements suffer from unrealistic sampling requirements, from unfeasible computational complexities, and, most severely, from the lacking reliability of nonlinear and nonconvex formulations. As an alternative, we propose a partially coherent (PC) multi-probe measurement technique and an associated linear reconstruction method which mitigate all these issues. Hence, reliable and accurate phase retrieval can be achieved in near-field far-field transformations (NFFFTs). In particular, we resolve the issues related to open measurement surfaces (as they may emerge in drone-based measurement setups) and we highlight the importance of considering the measurement setup and the phaseless NFFFT simultaneously. Specifically, the influence of special multi-probe arrangements on the reconstruction quality of PC solvers is shown.
Nonlinear Attitude Estimation Using Intermittent Linear Velocity and Vector Measurements
Authors: Miaomiao Wang, Abdelhamid Tayebi
Date: 2021-09-22 00:00:00

This paper investigates the problem of continuous attitude estimation on $SO(3)$ using continuous angular velocity and linear acceleration measurements as well as intermittent linear velocity and inertial vector measurements. First, we propose a nonlinear observer for the case where all the measurements are continuous and almost global asymptotic stability (AGAS) is shown using the notion of almost global input-to-state stability (ISS) on manifolds. Thereafter, a hybrid attitude observer, with AGAS guarantees, is proposed in terms of intermittent linear velocity and vector measurements. Numerical simulation results are presented to illustrate the performance of the proposed hybrid observer.
Stigmergy-based collision-avoidance algorithm for self-organising swarms
Authors: Paolo Grasso, Mauro Sebastián Innocente
Date: 2021-09-22 00:00:00
Tasks: Collision Avoidance, Diversity

Real-time multi-agent collision-avoidance algorithms comprise a key enabling technology for the practical use of self-organising swarms of drones. This paper proposes a decentralised reciprocal collision-avoidance algorithm, which is based on stigmergy and scalable. The algorithm is computationally inexpensive, based on the gradient of the locally measured dynamic cumulative signal strength field which results from the signals emitted by the swarm. The signal strength acts as a repulsor on each drone, which then tends to steer away from the noisiest regions (cluttered environment), thus avoiding collisions. The magnitudes of these repulsive forces can be tuned to control the relative importance assigned to collision avoidance with respect to the other phenomena affecting the agent's dynamics. We carried out numerical experiments on a self-organising swarm of drones aimed at fighting wildfires autonomously. As expected, it has been found that the collision rate can be reduced either by decreasing the cruise speed of the agents and/or by increasing the sampling frequency of the global signal strength field. A convenient by-product of the proposed collision-avoidance algorithm is that it helps maintain diversity in the swarm, thus enhancing exploration.
Towards cyber-physical systems robust to communication delays: A differential game approach
Authors: Shankar A. Deka, Donggun Lee, Claire J. Tomlin
Date: 2021-09-21 00:00:00
Tasks: Autonomous Vehicles

Collaboration between interconnected cyber-physical systems is becoming increasingly pervasive. Time-delays in communication channels between such systems are known to induce catastrophic failure modes, like high frequency oscillations in robotic manipulators in bilateral teleoperation or string instability in platoons of autonomous vehicles. This paper considers nonlinear time-delay systems representing coupled robotic agents, and proposes controllers that are robust to time-varying communication delays. We introduce approximations that allow the delays to be considered as implicit control inputs themselves, and formulate the problem as a zero-sum differential game between the stabilizing controllers and the delays acting adversarially. The ensuing optimal control law is finally compared to known results from Lyapunov-Krasovskii based approaches via numerical experiments.
Formulations and Approximations of the Branch Flow Model for Mesh Power Networks
Authors: Zhao Yuan
Date: 2021-09-22 00:00:00
Tasks: Computational Efficiency

The formulations and approximations of the branch flow model for mesh power networks (Mesh-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact Mesh-BranchFlow model are listed. These six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming (SOCP) are then used to derive the six formats of the convex Mesh-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line $\Pi$-model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line $\Pi$-model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the Mesh-BranchFlow model in deriving twelve formats of the exact optimal power flow (OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the Mesh-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate Mesh-BranchFlow model.
Stability Assessment for Multi-Infeed Grid-Connected VSCs Modeled in the Admittance Matrix Form
Authors: Luis Orellana, Luis Sainz, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2021-09-22 00:00:00
Tasks: Form

The increasing use of power electronics converters to integrate renewable energy sources has been subject of concern due to the resonance oscillatory phenomena caused by their interaction with poorly damped AC networks. Early studies are focused to assess the controller influence of a single converter connected to simple networks, and they are no longer representative for existing systems. Lately, studies of multi-infeed grid-connected converters are of particular interest, and their main aim is to apply traditional criteria and identify their difficulties in the stability assessment. An extension of traditional criteria is commonly proposed as a result of these analysis, but they can be burdensome for large and complex power systems. The present work addresses this issue by proposing a simple criterion to assess the stability of large power systems with high-penetration of power converters. The criterion has its origin in the mode analysis and positive-net damping stability criteria, and it addresses the stability in the frequency domain by studying the eigenvalues magnitude and real component of dynamic models in the admittance matrix form. Its effectiveness is tested in two case studies developed in Matlab/Simulink which compare it with traditionally criteria, proving its simplicity.