pwc-archive/papers-with-abstracts huggingface dataset:
Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering
Authors: Maciej P. Polak, Dane Morgan
Date: 2023-03-07 00:00:00
Tasks: Prompt Engineering

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data's correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data we find precision and recall both close to 90% from the best conversational LLMs, like ChatGPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract.
Early Warning Signals of Social Instabilities in Twitter Data
Authors: Vahid Shamsaddini, Henry Kirveslahti, Raphael Reinauer, Wallyson Lemes de Oliveira, Matteo Caorsi, Etienne Voutaz
Date: 2023-03-03 00:00:00
Tasks: Anomaly Detection

The goal of this project is to create and study novel techniques to identify early warning signals for socially disruptive events, like riots, wars, or revolutions using only publicly available data on social media. Such techniques need to be robust enough to work on real-time data: to achieve this goal we propose a topological approach together with more standard BERT models. Indeed, topology-based algorithms, being provably stable against deformations and noise, seem to work well in low-data regimes. The general idea is to build a binary classifier that predicts if a given tweet is related to a disruptive event or not. The results indicate that the persistent-gradient approach is stable and even more performant than deep-learning-based anomaly detection algorithms. We also benchmark the generalisability of the methodology against out-of-samples tasks, with very promising results.
Algorithmic neutrality
Authors: Milo Phillips-Brown
Date: 2023-03-09 00:00:00

Algorithms wield increasing control over our lives: over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?
Improving Recommendation Systems with User Personality Inferred from Product Reviews
Authors: Xinyuan Lu, Min-Yen Kan
Date: 2023-03-09 00:00:00
Tasks: Decision Making, Music Recommendation, Product Recommendation, Recommendation Systems

Personality is a psychological factor that reflects people's preferences, which in turn influences their decision-making. We hypothesize that accurate modeling of users' personalities improves recommendation systems' performance. However, acquiring such personality profiles is both sensitive and expensive. We address this problem by introducing a novel method to automatically extract personality profiles from public product review text. We then design and assess three context-aware recommendation architectures that leverage the profiles to test our hypothesis. Experiments on our two newly contributed personality datasets -- Amazon-beauty and Amazon-music -- validate our hypothesis, showing performance boosts of 3--28%.Our analysis uncovers that varying personality types contribute differently to recommendation performance: open and extroverted personalities are most helpful in music recommendation, while a conscientious personality is most helpful in beauty product recommendation.
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence
Authors: Yunyun Wang, Tatsushi Oka, Dan Zhu
Date: 2023-03-09 00:00:00
Tasks: Time Series, Time Series Analysis

Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we expand the scope of vector autoregression by incorporating a multivariate distributional regression framework and introducing a distributional impulse response function, providing a comprehensive view of dynamic heterogeneity. We propose a straightforward yet flexible estimation method and establish its asymptotic properties under weak dependence assumptions. Our empirical analysis examines the conditional joint distribution of GDP growth and financial conditions in the United States, with a focus on the global financial crisis. Our results show that tight financial conditions lead to a multimodal conditional joint distribution of GDP growth and financial conditions, and easing financial conditions significantly impacts long-term GDP growth, while improving the GDP growth during the global financial crisis has limited effects on financial conditions.
An IEEE 802.11a/g/p OFDM Receiver for GNU Radio
Authors: Bloessl
Date: 2018-05-01 00:00:00
Proceeding: 2018-5-1 2018 5

We present a complete simulation and experimentation framework for IEEE 802.11p. The core of the framework is a Software Defined Radio (SDR)-based Orthogonal Frequency Division Multiplexing (OFDM) transceiver that we validated extensively by means of simulations, interoperability tests, and, ultimately, by conducting a field test. Being SDR-based, the transceiver offers important benefits: It provides access to all data down to and including the physical layer, allowing for a better understanding of the system. Based on open and programmable hardware and software, the transceiver is completely transparent and all implementation details can be studied and, if needed, modified. Finally, it enables a seamless switch between simulations and experiments and, thus, helps to bridge the gap between theory and practice. Comparing the transceiver’s performance with independent results from simulations and experiments, we underline its potential to be used as a tool for further studies of IEEE 802.11p networks both in field operational tests as well as for simulation-based development of novel physical layer solutions. To make the framework accessible to fellow researchers and to allow reproduction of the results, we released it under an Open Source license.
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution
Authors: Jianping Cai, Ximeng Liu, Zhiyong Yu, Kun Guo, Jiayin Li
Date: 2022-10-26 00:00:00
Proceeding: IEEE Transactions on Emerging Topics in Computing 2022 10
Tasks: Data Integration, Federated Learning, Vertical Federated Learning

Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples' modeling needs, we propose two vertical federation learning algorithms for ridge regression via least-squares solution for two-party and multi-party scenarios, respectively. Compared with the state-of-the-art algorithms, our algorithms only need one round of calculation for the optimization instead of iteration. Furthermore, our algorithms can effectively handle large-scale samples due to the number of cryptographic operations in our algorithms being independent of the number of samples. Through our proposed the matrix secure agent computing theory and $\delta$ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.δ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.
Genetics of Lactose Intolerance: An Updated Review and Online Interactive World Maps of Phenotype and Genotype Frequencies
Authors: Augusto Anguita-Ruiz, Concepción M. Aguilera, Ángel Gil
Date: 2020-09-03 00:00:00
Proceeding: Nutrients 2020 9

In humans the ability to digest milk lactose is conferred by a β-galactosidase enzyme called lactase-phlorizin hydrolase (LPH). While in some humans (approximately two-thirds of humankind) the levels of this enzyme decline drastically after the weaning phase (a trait known as lactase non-persistence (LNP)), some other individuals are capable of maintaining high levels of LPH lifelong (lactase persistence (LP)), thus being able to digest milk during adulthood. Both lactase phenotypes in humans present a complex genetic basis and have been widely investigated during the last decades. The distribution of lactase phenotypes and their associated single nucleotide polymorphisms (SNPs) across human populations has also been extensively studied, though not recently reviewed. All available information has always been presented in the form of static world maps or large dimension tables, so that it would benefit from the newly available visualization tools, such as interactive world maps. Taking all this into consideration, the aims of the present review were: (1) to gather and summarize all available information on LNP and LP genetic mechanisms and evolutionary adaptation theories, and (2) to create online interactive world maps, including all LP phenotype and genotype frequency data reported to date. As a result, we have created two online interactive resources, which constitute an upgrade over previously published static world maps, and allow users a personalized data exploration, while at the same time accessing complete reports by population or ethnicity.
KnowRob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-enabled Robotic Agents
Authors: Michael Beetz, Daniel Beßler, Andrei Haidu, Mihai Pomarlan, Asil Kaan Bozcuo ̆glu, Georg Bartels
Date: 2018-05-21 00:00:00
Proceeding: 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018 5
Tasks: Motion Planning

Abstract— In this paper we present K NOW R OB 2, a second
generation knowledge representation and reasoning framework
for robotic agents. K NOW R OB 2 is an extension and partial
redesign of K NOW R OB , currently one of the most advanced
knowledge processing systems for robots that has enabled them
to successfully perform complex manipulation tasks such as
making pizza, conducting chemical experiments, and setting
tables. The knowledge base appears to be a conventional first-
order time interval logic knowledge base, but it exists to a large
part only virtually: many logical expressions are constructed on
demand from data structures of the control program, computed
through robotics algorithms including ones for motion planning
and solving inverse kinematics problems, and log data stored in
noSQL databases. Novel features and extensions of K NOW R OB 2
substantially increase the capabilities of robotic agents of
acquiring open-ended manipulation skills and competence,
reasoning about how to perform manipulation actions more
realistically, and acquiring commonsense knowledge.
An Observer-Based Key Agreement Scheme for Remotely Controlled Mobile Robots
Authors: Amir Mohammad Naseri, Walter Lucia, Amr Youssef
Date: 2023-03-08 00:00:00

Remotely controlled mobile robots are important examples of Cyber-Physical Systems (CPSs). Recently, these robots are being deployed in many safety critical applications. Therefore, ensuring their cyber-security is of paramount importance. Different control schemes that have been proposed to secure such systems against sophisticated cyber-attacks require the exchange of secret messages between their smart actuators and the remote controller. Thus, these schemes require pre-shared secret keys, or an established Public Key Infrastructure (PKI) that allows for key agreement. Such cryptographic approaches might not always be suitable for the deployment environments of such remotely mobile robots. To address this problem, in this paper, we consider a control theoretic approach for establishing a secret key between the remotely controlled robot and the networked controller without resorting to traditional cryptographic techniques. Our key agreement scheme leverages a nonlinear unknown input observer and an error correction code mechanism to allow the robot to securely agree on a secret key with its remote controller. To validate the proposed scheme, we implement it using a Khepera-IV differential drive robot and evaluate its efficiency and the additional control cost acquired by it. Our experimental results confirm the effectiveness of the proposed key establishment scheme.
Practical Statistical Considerations for the Clinical Validation of AI/ML-enabled Medical Diagnostic Devices
Authors: Feiming Chen, Hong Laura Lu, Arianna Simonetti
Date: 2023-03-02 00:00:00
Tasks: Diagnostic

Artificial Intelligence (AI) and Machine-Learning (ML) models have been increasingly used in medical products, such as medical device software. General considerations on the statistical aspects for the evaluation of AI/ML-enabled medical diagnostic devices are discussed in this paper. We also provide relevant academic references and note good practices in addressing various statistical challenges in the clinical validation of AI/ML-enabled medical devices in the context of their intended use.
In search of the most efficient and memory-saving visualization of high dimensional data
Authors: Bartosz Minch
Date: 2023-02-27 00:00:00
Tasks: CPU, Dimensionality Reduction, GPU

Interactive exploration of large, multidimensional datasets plays a very important role in various scientific fields. It makes it possible not only to identify important structural features and forms, such as clusters of vertices and their connection patterns, but also to evaluate their interrelationships in terms of position, distance, shape and connection density. We argue that the visualization of multidimensional data is well approximated by the problem of two-dimensional embedding of undirected nearest-neighbor graphs. The size of complex networks is a major challenge for today's computer systems and still requires more efficient data embedding algorithms. Existing reduction methods are too slow and do not allow interactive manipulation. We show that high-quality embeddings are produced with minimal time and memory complexity. We present very efficient IVHD algorithms (CPU and GPU) and compare them with the latest and most popular dimensionality reduction methods. We show that the memory and time requirements are dramatically lower than for base codes. At the cost of a slight degradation in embedding quality, IVHD preserves the main structural properties of the data well with a much lower time budget. We also present a meta-algorithm that allows the use of any unsupervised data embedding method in a supervised manner.
An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimation
Authors: Alberto Bertipaglia, Mohsen Alirezaei, Riender Happee, Barys Shyrokau
Date: 2023-03-09 00:00:00
Tasks: Diversity

This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on a non-linear single-track vehicle model to enhance the estimation accuracy of a Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, and both use the standard inertial measurement unit and the tyre forces measured by load sensing technology. CNN benefits from the UKF the capacity to leverage the laws of physics. Concurrently, the UKF uses the CNN outputs as sideslip angle pseudo-measurement and adaptive process noise parameters. The back-propagation through time algorithm is applied end-to-end to the CNN and the UKF to employ the mutualistic property. Using a large-scale experimental dataset of 216 manoeuvres containing a great diversity of vehicle behaviours, we demonstrate a significant improvement in the accuracy of the proposed architecture over the current state-of-art hybrid approach combined with model-based and data-driven techniques. In the case that a limited dataset is provided for the training phase, the proposed hybrid approach still guarantees estimation robustness.
Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
Authors: J. Matoušek, J. Duník, M. Brandner
Date: 2023-03-09 00:00:00
Tasks: State Estimation

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel efficient PMF (ePMF) estimator, unifying continuous and discrete, approaches is proposed, designed, and discussed. By numerical illustrations, it is shown, that the proposed ePMF can lead to a time complexity reduction that exceeds 99.9% without compromising accuracy. The MATLAB code of the ePMF is released with this paper.
Designing Automated Vehicle and Traffic Systems towards Meaningful Human Control
Authors: Simeon C. Calvert, Stig Johnsen, Ashwin George
Date: 2023-03-09 00:00:00

Ensuring operational control over automated vehicles is not trivial and failing to do so severely endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part including drivers, occupants, vehicle designers and governments. While progress is being made, a comprehensive approach to the problem is being ignored, which can be solved in the main through considering Meaningful Human Control (MHC). In this research, an Integrated System Proximity framework and Operational Process Design approach to assist the development of Connected Automated Vehicles (CAV) under the consideration of MHC are introduced. These offer a greater understanding and basis for vehicle and traffic system design by vehicle designers and governments as two important influencing stakeholders. The framework includes an extension to a system approach, which also considers ways that MHC can be improved through updating: either implicit proximal updating or explicit distal updating. The process and importance are demonstrated in three recent cases from practice. Finally, a call for action is made to government and regulatory authorities, as well as the automotive industry, to ensure that MHC processes are explicitly included in policy, regulations, and design processes to ensure future ad-vancement of CAVs in a responsible, safe and humanly agreeable fashion.
Parallel Computing Based Solution for Reliability-Constrained Distribution Network Planning
Authors: Yaqi Sun, Wenchuan Wu, Yi Lin, Hai Huang, Hao Chen
Date: 2023-03-09 00:00:00

The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially for large scale DNs. In this paper, we propose a parallel computing-based acceleration algorithm for solve RcDNP problem. The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination. Then a parallelizable augmented Lagrangian algorithm with acceleration strategy is developed to solve the coordination planning problems. In this method, the lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem. To address the presence of nonconvexity, Gauss-Seidel iteration is adopted on the convex hull of the feasible region constructed by the decomposition method. Under mild conditions, the optimality and convergence of this algorithm is proven. The numerical tests show the proposed method can significantly reduce the computation time and make the RcDNP applicable for real-world problems.
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning
Authors: Purba Mukherjee, Rahul Shah, Arko Bhaumik, Supratik Pal
Date: 2023-03-09 00:00:00
Tasks: Gaussian Processes

We study the prospects of Gaussian processes (GP), a machine learning (ML) algorithm, as a tool to reconstruct the Hubble parameter $H(z)$ with two upcoming gravitational wave missions, namely the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). Assuming various background cosmological models, the Hubble parameter has been reconstructed in a non-parametric manner with the help of GP using realistically generated catalogs for each mission. The effects of early-time and late-time priors on the reconstruction of $H(z)$, and hence on the Hubble constant ($H_0$), have also been focused on separately. Our analysis reveals that GP is quite robust in reconstructing the expansion history of the Universe within the observational window of the specific missions under consideration. We further confirm that both eLISA and ET would be able to provide constraints on $H(z)$ and $H_0$ which would be competitive to those inferred from current datasets. In particular, we observe that an eLISA run of $\sim10$-year duration with $\sim80$ detected bright siren events would be able to constrain $H_0$ as good as a $\sim3$-year ET run assuming $\sim 1000$ bright siren event detections. Further improvement in precision is expected for longer eLISA mission durations such as a $\sim15$-year time-frame having $\sim120$ events. Lastly, we discuss the possible role of these future gravitational wave missions in addressing the Hubble tension, for each model, on a case-by-case basis.