pwc-archive/papers-with-abstracts huggingface dataset:
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
Authors: Andreea Iana, Mehwish Alam, Alexander Grote, Nevena Nikolajevic, Katharina Ludwig, Philipp Müller, Christof Weinhardt, Heiko Paulheim
Date: 2023-09-01 00:00:00
Tasks: Articles, Benchmarking, Knowledge Graphs, News Recommendation

News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics. Given the crucial impact that media plays in opinion formation, especially for sensitive topics, understanding the effects of personalized recommendation beyond accuracy has become essential in today's digital society. In this work, we present NeMig, a bilingual news collection on the topic of migration, and corresponding rich user data. In comparison to existing news recommendation datasets, which comprise a large variety of monolingual news, NeMig covers articles on a single controversial topic, published in both Germany and the US. We annotate the sentiment polarization of the articles and the political leanings of the media outlets, in addition to extracting subtopics and named entities disambiguated through Wikidata. These features can be used to analyze the effects of algorithmic news curation beyond accuracy-based performance, such as recommender biases and the creation of filter bubbles. We construct domain-specific knowledge graphs from the news text and metadata, thus encoding knowledge-level connections between articles. Importantly, while existing datasets include only click behavior, we collect user socio-demographic and political information in addition to explicit click feedback. We demonstrate the utility of NeMig through experiments on the tasks of news recommenders benchmarking, analysis of biases in recommenders, and news trends analysis. NeMig aims to provide a useful resource for the news recommendation community and to foster interdisciplinary research into the multidimensional effects of algorithmic news curation.
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation
Authors: Yuxuan Zhang, Wei Yang, Shaowei Wang
Date: 2023-08-01 00:00:00
Proceeding: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence Main Track 2023 8
Tasks: Few-Shot Semantic Segmentation

Current few-shot segmentation (FSS) approaches have made tremendous achievements based on prototypical learning techniques. However, due to the scarcity of the support data provided, FSS methods still suffer from the intra-class and inter-class gaps. In this paper, we propose a uniform network to fill both the gaps, termed FGNet. It consists of the novel design of a Self-Adaptive Module (SAM) to emphasize the query feature to generate an enhanced prototype for self-alignment. Such a prototype caters to each query sample itself since it contains the underlying intra-instance information, which gets around the intra-class appearance gap. Moreover, we design an Inter-class Feature Separation Module (IFSM) to separate the feature space of the target class from other classes, which contributes to bridging the inter-class gap. In addition, we present several new losses and a method termed B-SLIC, which help to further enhance the separation performance of FGNet. Experimental results show that FGNet reduces both the gaps for FSS by SAM and IFSM respectively, and achieves state-of-the-art performances on both PASCAL-5i and COCO-20i datasets compared with previous top-performing approaches.
Bayesian estimation and reconstruction of marine surface contaminant dispersion
Authors: Yang Liu, Christopher M. Harvey, Frederick E. Hamlyn, Cunjia Liu
Date: 2023-09-01 00:00:00

Discharge of hazardous substances into the marine environment poses a substantial risk to both public health and the ecosystem. In such incidents, it is imperative to accurately estimate the release strength of the source and reconstruct the spatio-temporal dispersion of the substances based on the collected measurements. In this study, we propose an integrated estimation framework to tackle this challenge, which can be used in conjunction with a sensor network or a mobile sensor for environment monitoring. We employ the fundamental convection-diffusion partial differential equation (PDE) to represent the general dispersion of a physical quantity in a non-uniform flow field. The PDE model is spatially discretised into a linear state-space model using the dynamic transient finite-element method (FEM) so that the characterisation of time-varying dispersion can be cast into the problem of inferring the model states from sensor measurements. We also consider imperfect sensing phenomena, including miss-detection and signal quantisation, which are frequently encountered when using a sensor network. This complicated sensor process introduces nonlinearity into the Bayesian estimation process. A Rao-Blackwellised particle filter (RBPF) is designed to provide an effective solution by exploiting the linear structure of the state-space model, whereas the nonlinearity of the measurement model can be handled by Monte Carlo approximation with particles. The proposed framework is validated using a simulated oil spill incident in the Baltic sea with real ocean flow data. The results show the efficacy of the developed spatio-temporal dispersion model and estimation schemes in the presence of imperfect measurements. Moreover, the parameter selection process is discussed, along with some comparison studies to illustrate the advantages of the proposed algorithm over existing methods.
BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants
Authors: Woowon Jang, Shiwoo Koak, Jiwon Im, Utku Ozbulak, Joris Vankerschaver
Date: 2023-09-01 00:00:00

BRCA genes, comprising BRCA1 and BRCA2 play indispensable roles in preserving genomic stability and facilitating DNA repair mechanisms. The presence of germline mutations in these genes has been associated with increased susceptibility to various cancers, notably breast and ovarian cancers. Recent advancements in cost-effective sequencing technologies have revolutionized the landscape of cancer genomics, leading to a notable rise in the number of sequenced cancer patient genomes, enabling large-scale computational studies. In this study, we delve into the BRCA mutations in the dbSNP, housing an extensive repository of 41,177 and 44,205 genetic mutations for BRCA1 and BRCA2, respectively. Employing meticulous computational analysis from an umbrella perspective, our research unveils intriguing findings pertaining to a number of critical aspects. Namely, we discover that the majority of BRCA mutations in dbSNP have unknown clinical significance. We find that, although exon 11 for both genes contains the majority of the mutations and may seem as if it is a mutation hot spot, upon analyzing mutations per base pair, we find that all exons exhibit similar levels of mutations. Investigating mutations within introns, while we observe that the recorded mutations are generally uniformly distributed, almost all of the pathogenic mutations in introns are located close to splicing regions (at the beginning or the end). In addition to the findings mentioned earlier, we have also made other discoveries concerning mutation types and the level of confidence in observations within the dbSNP database.
Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments
Authors: Georgios Kopanas, George Drettakis
Date: 2023-08-24 00:00:00
Tasks: 3D Reconstruction, NeRF, Novel View Synthesis

Neural Radiance Fields, or NeRFs, have drastically improved novel view synthesis and 3D reconstruction for rendering. NeRFs achieve impressive results on object-centric reconstructions, but the quality of novel view synthesis with free-viewpoint navigation in complex environments (rooms, houses, etc) is often problematic. While algorithmic improvements play an important role in the resulting quality of novel view synthesis, in this work, we show that because optimizing a NeRF is inherently a data-driven process, good quality data play a fundamental role in the final quality of the reconstruction. As a consequence, it is critical to choose the data samples -- in this case the cameras -- in a way that will eventually allow the optimization to converge to a solution that allows free-viewpoint navigation with good quality. Our main contribution is an algorithm that efficiently proposes new camera placements that improve visual quality with minimal assumptions. Our solution can be used with any NeRF model and outperforms baselines and similar work.
Signal Processing and Learning for Next Generation Multiple Access in 6G
Authors: Wei Chen, Yuanwei Liu, Hamid Jafarkhani, Yonina C. Eldar, Peiying Zhu, Khaled B Letaief
Date: 2023-09-01 00:00:00

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning, e.g., deep learning, provide promising approaches to deal with complex and previously intractable problems. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed.
Instabilities of Super-Time-Stepping Methods on the Heston Stochastic Volatility Model
Authors: Fabien Le Floc'h
Date: 2023-09-01 00:00:00

This note explores in more details instabilities of explicit super-time-stepping schemes, such as the Runge-Kutta-Chebyshev or Runge-Kutta-Legendre schemes, noticed in the litterature, when applied to the Heston stochastic volatility model. The stability remarks are relevant beyond the scope of super-time-stepping schemes.
On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions
Authors: Tamas G. Molnar, Gabor Orosz, Aaron D. Ames
Date: 2023-08-31 00:00:00

Connected automated vehicles have shown great potential to improve the efficiency of transportation systems in terms of passenger comfort, fuel economy, stability of driving behavior and mitigation of traffic congestions. Yet, to deploy these vehicles and leverage their benefits, the underlying algorithms must ensure their safe operation. In this paper, we address the safety of connected cruise control strategies for longitudinal car following using control barrier function (CBF) theory. In particular, we consider various safety measures such as minimum distance, time headway and time to conflict, and provide a formal analysis of these measures through the lens of CBFs. Additionally, motivated by how stability charts facilitate stable controller design, we derive safety charts for existing connected cruise controllers to identify safe choices of controller parameters. Finally, we combine the analysis of safety measures and the corresponding stability charts to synthesize safety-critical connected cruise controllers using CBFs. We verify our theoretical results by numerical simulations.
Remixing-based Unsupervised Source Separation from Scratch
Authors: Kohei Saijo, Tetsuji Ogawa
Date: 2023-09-01 00:00:00
Tasks: Self-Supervised Learning

We propose an unsupervised approach for training separation models from scratch using RemixIT and Self-Remixing, which are recently proposed self-supervised learning methods for refining pre-trained models. They first separate mixtures with a teacher model and create pseudo-mixtures by shuffling and remixing the separated signals. A student model is then trained to separate the pseudo-mixtures using either the teacher's outputs or the initial mixtures as supervision. To refine the teacher's outputs, the teacher's weights are updated with the student's weights. While these methods originally assumed that the teacher is pre-trained, we show that they are capable of training models from scratch. We also introduce a simple remixing method to stabilize training. Experimental results demonstrate that the proposed approach outperforms mixture invariant training, which is currently the only available approach for training a monaural separation model from scratch.
Integrated Charging Scheduling and Operational Control for an Electric Bus Network
Authors: Rémi Lacombe, Nikolce Murgovski, Sébastien Gros, Balázs Kulcsár
Date: 2023-09-01 00:00:00
Tasks: Scheduling

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. We propose a hierarchical control framework to solve this problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions together with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.
Iterative, Small-Signal L2 Stability Analysis of Nonlinear Constrained Systems
Authors: Reza Lavaei, Leila J. Bridgeman
Date: 2023-09-01 00:00:00

This paper provides a method to analyze the small-signal L2 gain of control-affine nonlinear systems on compact sets via iterative semi-definite programs. First, a continuous piecewise affine storage function and the corresponding upper bound on the L2 gain are found on a bounded, compact set's triangulation. Then, to ensure that the state does not escape this set, a barrier function is found that is robust to small-signal inputs. Small-signal L2 stability then holds inside each sublevel set of the barrier function inside the set where the storage function was found. The bound on the inputs is also found while searching for a barrier function. The method's effectiveness is shown in a numerical example.
Data-Driven Safety Filter: An Input-Output Perspective
Authors: Mohammad Bajelani, Klaske van Heusden
Date: 2023-09-01 00:00:00
Tasks: State Estimation

Implementation of learning-based control remains challenging due to the absence of safety guarantees. Safe control methods have turned to model-based safety filters to address these challenges, but this is paradoxical when the ultimate goal is a model-free, data-driven control solution. Addressing the core question of "Can we ensure the safety of any learning-based algorithm without explicit prediction models and state estimation?" this paper proposes a Data-Driven Safety Filter (DDSF) grounded in Behavioral System Theory (BST). The proposed method needs only a single system trajectory available in an offline dataset to modify unsafe learning inputs to safe inputs. This contribution addresses safe control in the input-output framework and therefore does not require full state measurements or explicit state estimation. Since no explicit model is required, the proposed safe control solution is not affected by unmodeled dynamics and unstructured uncertainty and can provide a safe solution for systems with unknown time delays. The effectiveness of the proposed DDSF is illustrated in simulation for a high-order six-degree-of-freedom aerial robot and a time-delay adaptive cruise control system.
Precision Enhancement of Distribution System State Estimation via Tri-Objective Micro Phasor Measurement Unit Deployment
Authors: Arya Abdolahi, Navid Taghizadegan Kalantari
Date: 2023-08-31 00:00:00
Tasks: Sensitivity, State Estimation

A tri-objective optimal Micro Phasor Measurement Units ({\mu}-PMUs) Placement method is presented, with a focus on minimizing the following three parameters: i) the total number of {\mu}-PMU channels, (ii) the maximum state estimation uncertainty, and (iii) the sensitivity of state estimation to line parameter tolerances. The suggested formulation takes single-line and {\mu}-PMU failures into consideration while guaranteeing the complete observability of the system in the presence and absence of contingencies. It also takes into account the impact of zero injection nodes and the quantity of {\mu}-PMU channels carried out at every node. The suggested placement issue is addressed using a customized version of the nondominated sorting genetic algorithm II (NSGA-II). According to the results achieved utilizing three test systems of varying sizes, {\mu}-PMU channels beyond predetermined thresholds only result in higher costs and negligible further decreases in state estimation uncertainty and sensitivity to line parameter tolerances. Additionally, we may omit to instrument between 30 and 40% of buses if {\mu}-PMUs with only two three-phase channels are utilized, with only a modest negative effect on state estimate performance even in the event of contingencies.
Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation
Authors: Can Deniz Bezek, Maxim Haas, Richard Rau, Orcun Goksel
Date: 2023-09-01 00:00:00
Tasks: Image Reconstruction

Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS distributions, spatial domain reconstruction from relative speckle shifts between different beamforming sequences is a promising approach. This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved. The reconstruction accuracy thus highly depends on the hand-crafted forward imaging model. In this work, we propose to learn the SoS imaging model based on data. We introduce a convolutional formulation of the pulse-echo SoS imaging problem such that the entire field-of-view requires a single unified kernel, the learning of which is then tractable and robust. We present least-squares estimation of such convolutional kernel, which can further be constrained and regularized for numerical stability. In experiments, we show that a forward model learned from k-Wave simulations improves the median contrast of SoS reconstructions by 63%, compared to a conventional hand-crafted line-based wave-path model. This simulation-learned model generalizes successfully to acquired phantom data, nearly doubling the SoS contrast compared to the conventional hand-crafted alternative. We demonstrate equipment-specific and small-data regime feasibility by learning a forward model from a single phantom image, where our learned model quadruples the SoS contrast compared to the conventional hand-crafted model. On in-vivo data, the simulation- and phantom-learned models respectively exhibit impressive 7 and 10 folds contrast improvements over the conventional model.
New general dependence measures: construction, estimation and application to high-frequency stock returns
Authors: Aleksy Leeuwenkamp, Wentao Hu
Date: 2023-08-31 00:00:00
Tasks: Management

We propose a set of dependence measures that are non-linear, local, invariant to a wide range of transformations on the marginals, can show tail and risk asymmetries, are always well-defined, are easy to estimate and can be used on any dataset. We propose a nonparametric estimator and prove its consistency and asymptotic normality. Thereby we significantly improve on existing (extreme) dependence measures used in asset pricing and statistics. To show practical utility, we use these measures on high-frequency stock return data around market distress events such as the 2010 Flash Crash and during the GFC. Contrary to ubiquitously used correlations we find that our measures clearly show tail asymmetry, non-linearity, lack of diversification and endogenous buildup of risks present during these distress events. Additionally, our measures anticipate large (joint) losses during the Flash Crash while also anticipating the bounce back and flagging the subsequent market fragility. Our findings have implications for risk management, portfolio construction and hedging at any frequency.
Preventing Others from Commercializing Your Innovation: Evidence from Creative Commons Licenses
Authors: Erdem Dogukan Yilmaz, Tim Meyer, Milan Miric
Date: 2023-09-01 00:00:00

Online innovation communities are an important source of innovation for many organizations. While contributions to such communities are typically made without financial compensation, these contributions are often governed by licenses such as Creative Commons that may prevent others from building upon and commercializing them. While this can diminish the usefulness of contributions, there is limited work analyzing what leads individuals to impose restrictions on the use of their work. In this paper, we examine innovators imposing restrictive licenses within the 3D-printable design community Thingiverse. Our analyses suggest that innovators are more likely to restrict commercialization of their contributions as their reputation increases and when reusing contributions created by others. These findings contribute to innovation communities and the growing literature on property rights in digital markets.
Accurate Quality Elicitation in a Multi-Attribute Choice Setting
Authors: Changkuk Im
Date: 2023-08-31 00:00:00
Tasks: Attribute

This paper studies how to accurately elicit quality for alternatives with multiple attributes. Two multiple price lists (MPLs) are considered: (i) m-MPL which asks subjects to compare an alternative to money, and (ii) p-MPL where subjects are endowed with money and asked whether they would like to buy an alternative or not. Theoretical results show that m-MPL requires fewer assumptions for accurate quality elicitation compared to p-MPL. Experimental evidence from a within-subject experiment using consumer products shows that switch points between the two MPLs are different, which suggests that quality measures are sensitive to the elicitation method.