pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Personalized Relevance Algorithms for Directed Graphs
Authors: Luca Cavalcanti, Cristian Consonni, Martin Brugnara, David Laniado, Alberto Montresor
Date: 2024-05-03 00:00:00

We present an interactive Web platform that, given a directed graph, allows identifying the most relevant nodes related to a given query node. Besides well-established algorithms such as PageRank and Personalized PageRank, the demo includes Cyclerank, a novel algorithm that addresses some of their limitations by leveraging cyclic paths to compute personalized relevance scores. Our demo design enables two use cases: (a) algorithm comparison, comparing the results obtained with different algorithms, and (b) dataset comparison, for exploring and gaining insights into a dataset and comparing it with others. We provide 50 pre-loaded datasets from Wikipedia, Twitter, and Amazon and seven algorithms. Users can upload new datasets, and new algorithms can be easily added. By showcasing efficient algorithms to compute relevance scores in directed graphs, our tool helps to uncover hidden relationships within the data, which makes of it a valuable addition to the repertoire of graph analysis algorithms.
A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System
Authors: Yashar Deldjoo, Fatemeh Nazary
Date: 2024-05-03 00:00:00
Tasks: Benchmarking, Collaborative Filtering, counterfactual, Fairness, In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Recommendation Systems

The rapid adoption of large language models (LLMs) in recommender systems (RS) presents new challenges in understanding and evaluating their biases, which can result in unfairness or the amplification of stereotypes. Traditional fairness evaluations in RS primarily focus on collaborative filtering (CF) settings, which may not fully capture the complexities of LLMs, as these models often inherit biases from large, unregulated data. This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems (RecLLMs). We critically examine how fairness norms in classical RS fall short in addressing the challenges posed by LLMs. We argue that this gap can lead to arbitrary conclusions about fairness, and we propose a more structured, formal approach to evaluate fairness in such systems. Our experiments on the MovieLens dataset on consumer fairness, using in-context learning (zero-shot vs. few-shot) reveal fairness deviations in age-based recommendations, particularly when additional contextual examples are introduced (ICL-2). Statistical significance tests confirm that these deviations are not random, highlighting the need for robust evaluation methods. While this work offers a preliminary discussion on a proposed normative framework, our hope is that it could provide a formal, principled approach for auditing and mitigating bias in RecLLMs. The code and dataset used for this work will be shared at "gihub-anonymized".
How to Diversify any Personalized Recommender? A User-centric Pre-processing approach
Authors: Manel Slokom, Laura Hollink
Date: 2024-05-03 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems

In this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining recommendation performance. Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics. We personalize this strategy by selectively adding and removing a percentage of interactions from user profiles. This personalization ensures we remain closely aligned with user preferences while gradually introducing distribution shifts. Our pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture. To evaluate our approach, we run extensive experiments on two publicly available data sets for news and book recommendations. We test various standard and neural network-based recommender system algorithms. Our results show that our approach generates diverse recommendations, ensuring users are exposed to a wider range of items. Furthermore, leveraging pre-processed data for training leads to recommender systems achieving performance levels comparable to, and in some cases, better than those trained on original, unmodified data. Additionally, our approach promotes provider fairness by facilitating exposure to minority or niche categories.
Ah, that's the great puzzle: On the Quest of a Holistic Understanding of the Harms of Recommender Systems on Children
Authors: Robin Ungruh, Maria Soledad Pera
Date: 2024-05-03 00:00:00
Tasks: Recommendation Systems

Children come across various media items online, many of which are selected by recommender systems (RS) primarily designed for adults. The specific nature of the content selected by RS to display on online platforms used by children - although not necessarily targeting them as a user base - remains largely unknown. This raises questions about whether such content is appropriate given children's vulnerable stages of development and the potential risks to their well-being. In this position paper, we reflect on the relationship between RS and children, emphasizing the possible adverse effects of the content this user group might be exposed to online. As a step towards fostering safer interactions for children in online environments, we advocate for researchers, practitioners, and policymakers to undertake a more comprehensive examination of the impact of RS on children - one focused on harms. This would result in a more holistic understanding that could inform the design and deployment of strategies that would better suit children's needs and preferences while actively mitigating the potential harm posed by RS; acknowledging that identifying and addressing these harms is complex and multifaceted.
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems
Authors: Sanne Vrijenhoek, Savvina Daniil, Jorden Sandel, Laura Hollink
Date: 2024-05-03 00:00:00
Tasks: Diversity, Recommendation Systems

Diversity is a commonly known principle in the design of recommender systems, but also ambiguous in its conceptualization. Through semi-structured interviews we explore how practitioners at three different public service media organizations in the Netherlands conceptualize diversity within the scope of their recommender systems. We provide an overview of the goals that they have with diversity in their systems, which aspects are relevant, and how recommendations should be diversified. We show that even within this limited domain, conceptualization of diversity greatly varies, and argue that it is unlikely that a standardized conceptualization will be achieved. Instead, we should focus on effective communication of what diversity in this particular system means, thus allowing for operationalizations of diversity that are capable of expressing the nuances and requirements of that particular domain.
Deep Unfolding Tensor Rank Minimization With Generalized Detail Injection for Pansharpening
Authors: Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee
Date: 2024-04-22 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 4
Tasks: Pansharpening

Pansharpening aims to generate a high-resolution multispectral (HRMS) image by merging a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. While traditional model-based pansharpening algorithms have strong theoretical foundations, their performance, and generalizability are limited by handcrafted formulations. In contrast, recent deep learning (DL) approaches outperform model-based algorithms but do not effectively consider the physical properties of multispectral (MS) images, such as their spatial and spectral dependencies. These physical properties facilitate the exploitation of the actual imaging process, leading to enhanced spatial and spectral fidelities. In this work, we propose a deep unfolded tensor rank minimization framework with generalized detail injection for pansharpening to overcome the weaknesses of both model- and learning-based approaches while leveraging their advantages. Specifically, we first formulate the pansharpening task as a tensor rank minimization problem to exploit the low-rankness of MS images, providing a robust theoretical foundation on the physical properties of MS data. We also develop a generalized detail injection component, which effectively exploits the information in the PAN images, and incorporates it into the optimization to improve generalizability and representation capability. Then, we define a data-driven regularizer to compensate for modeling inaccuracies in the low-rank model and solve the optimization problem using an iterative technique. Finally, the iterative algorithm is unfolded into a multistage deep network, in which the optimization variables are solved by closed-form solutions and a data-driven regularizer in each stage. Experimental results on various MS image datasets demonstrate that the proposed algorithm achieves better pansharpening performance and interpretability than state-of-the-art algorithms.
Deep Unrolled Low-Rank Tensor Completion for High Dynamic Range Imaging
Authors: Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee
Date: 2022-09-01 00:00:00
Proceeding: IEEE Transactions on Image Processing 2022 9
Tasks: HDR Reconstruction, Image Generation, Multi-Exposure Image Fusion

The major challenge in high dynamic range (HDR) imaging for dynamic scenes is suppressing ghosting artifacts caused by large object motions or poor exposures. Whereas recent deep learning-based approaches have shown significant synthesis performance, interpretation and analysis of their behaviors are difficult and their performance is affected by the diversity of training data. In contrast, traditional model-based approaches yield inferior synthesis performance to learning-based algorithms despite their theoretical thoroughness. In this paper, we propose an algorithm unrolling approach to ghost-free HDR image synthesis algorithm that unrolls an iterative low-rank tensor completion algorithm into deep neural networks to take advantage of the merits of both learning- and model-based approaches while overcoming their weaknesses. First, we formulate ghost-free HDR image synthesis as a low-rank tensor completion problem by assuming the low-rank structure of the tensor constructed from low dynamic range (LDR) images and linear dependency among LDR images. We also define two regularization functions to compensate for modeling inaccuracy by extracting hidden model information. Then, we solve the problem efficiently using an iterative optimization algorithm by reformulating it into a series of subproblems. Finally, we unroll the iterative algorithm into a series of blocks corresponding to each iteration, in which the optimization variables are updated by rigorous closed-form solutions and the regularizers are updated by learned deep neural networks. Experimental results on different datasets show that the proposed algorithm provides better HDR image synthesis performance with superior robustness compared with state-of-the-art algorithms, while using significantly fewer training samples.
Reinforcement Learning control strategies for Electric Vehicles and Renewable energy sources Virtual Power Plants
Authors: Francesco Maldonato, Izgh Hadachi
Date: 2024-05-03 00:00:00

The increasing demand for direct electric energy in the grid is also tied to the increase of Electric Vehicle (EV) usage in the cities, which eventually will totally substitute combustion engine Vehicles. Nevertheless, this high amount of energy required, which is stored in the EV batteries, is not always used and it can constitute a virtual power plant on its own. Bidirectional EVs equipped with batteries connected to the grid can therefore charge or discharge energy depending on public needs, producing a smart shift of energy where and when needed. EVs employed as mobile storage devices can add resilience and supply/demand balance benefits to specific loads, in many cases as part of a Microgrid (MG). Depending on the direction of the energy transfer, EVs can provide backup power to households through vehicle-to-house (V2H) charging, or storing unused renewable power through renewable-to-vehicle (RE2V) charging. V2H and RE2V solutions can complement renewable power sources like solar photovoltaic (PV) panels and wind turbines (WT), which fluctuate over time, increasing the self-consumption and autarky. The concept of distributed energy resources (DERs) is becoming more and more present and requires new solutions for the integration of multiple complementary resources with variable supply over time. The development of these ideas is coupled with the growth of new AI techniques that will potentially be the managing core of such systems. Machine learning techniques can model the energy grid environment in such a flexible way that constant optimization is possible. This fascinating working principle introduces the wider concept of an interconnected, shared, decentralized grid of energy. This research on Reinforcement Learning control strategies for Electric Vehicles and Renewable energy sources Virtual Power Plants focuses on providing solutions for such energy supply optimization models.
Solving Neural Field Equations using Physics Informed Neural Networks
Authors: Weronika Wojtak, Estela Bicho, Wolfram Erlhagen
Date: 2023-09-17 00:00:00
Proceeding: International Conference of Numerical Analysis and Applied Mathematics, ICNAAM 2023 9
Tasks: Transfer Learning

This article presents an approach for solving neural field equations (NFEs) using Physics Informed Neural Networks
(PINNs). NFEs are integro-differential equations describing the spatio-temporal dynamics of neuronal populations in the cortex.
The traditional numerical methods for NFEs require significant computational effort due to the discretization of the spatial convolution.
The proposed approach leverages Fast Fourier Transforms (FFTs) to reduce the computational cost and improve efficiency. A
PINN, consisting of a surrogate network and a residual network, is trained to approximate the solutions of NFEs. The effectiveness
of the approach is demonstrated by solving the one-dimensional Amari equation, a commonly used neural field formulation. Our
results show that the accuracy of the PINN approach is comparable to traditional numerical methods. Future research directions include
optimizing hyperparameters, incorporating input terms in NFEs, exploring transfer learning, addressing the inverse problem,
and extending the approach to higher dimensions.
A dynamic neural field model of continuous input integration
Authors: Weronika Wojtak, Stephen Coombes, Daniele Avitabile, Estela Bicho, Wolfram Erlhagen
Date: 2021-08-21 00:00:00
Proceeding: Biological Cybernetics 2021 8

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a “bump” independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.
Neural Field Model for Measuring and Reproducing Time Intervals
Authors: Weronika Wojtak, Flora Ferreira, Estela Bicho, Wolfram Erlhagen
Date: 2019-09-09 00:00:00
Proceeding: 28th International Conference on Artificial Neural Networks, ICANN 2019 9

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.
Revolutionizing Retail Analytics: Advancing Inventory and Customer Insight with AI
Authors: A. Hossam, A. Ramadan, M. Magdy, R. Abdelwahab, S. Ashraf, Z. Mohamed
Date: 2024-02-24 00:00:00
Tasks: Demand Forecasting, Management, Marketing, Object Tracking

In response to the significant challenges facing the retail sector, including inefficient queue management, poor demand forecasting, and ineffective marketing, this paper introduces an innovative approach utilizing cutting-edge machine learning technologies. We aim to create an advanced smart retail analytics system (SRAS), leveraging these technologies to enhance retail efficiency and customer engagement. To enhance customer tracking capabilities, a new hybrid architecture is proposed integrating several predictive models. In the first stage of the proposed hybrid architecture for customer tracking, we fine-tuned the YOLOV8 algorithm using a diverse set of parameters, achieving exceptional results across various performance metrics. This fine-tuning process utilized actual surveillance footage from retail environments, ensuring its practical applicability. In the second stage, we explored integrating two sophisticated object-tracking models, BOT-SORT and ByteTrack, with the labels detected by YOLOV8. This integration is crucial for tracing customer paths within stores, which facilitates the creation of accurate visitor counts and heat maps. These insights are invaluable for understanding consumer behavior and improving store operations. To optimize inventory management, we delved into various predictive models, optimizing and contrasting their performance against complex retail data patterns. The GRU model, with its ability to interpret time-series data with long-range temporal dependencies, consistently surpassed other models like Linear Regression, showing 2.873% and 29.31% improvements in R2-score and mAPE, respectively.
Transforming Investment Strategies and Strategic Decision-Making: Unveiling a Novel Methodology for Enhanced Performance and Risk Management in Financial Markets
Authors: Tian Tian, Ricky Cooper, Jiahao Deng, Qingquan Zhang
Date: 2024-05-03 00:00:00
Tasks: Decision Making, Management, Portfolio Optimization

This paper introduces a novel methodology for index return forecasting, blending highly correlated stock prices, advanced deep learning techniques, and intricate factor integration. Departing from conventional cap-weighted approaches, our innovative framework promises to reimagine traditional methodologies, offering heightened diversification, amplified performance capture, and nuanced market depiction. At its core lies the intricate identification of highly correlated company clusters, fueling predictive accuracy and robustness. By harnessing these interconnected constellations, we unlock a profound comprehension of market dynamics, bestowing both investment entities and individual enterprises with invaluable performance insights. Moreover, our methodology integrates pivotal factors such as indexes and ETFs, seamlessly woven with Hierarchical Risk Parity (HRP) portfolio optimization, to elevate performance and fortify risk management. This comprehensive amalgamation refines risk diversification, fortifying portfolio resilience against turbulent market forces. The implications reverberate resoundingly. Investment entities stand poised to calibrate against competitors with surgical precision, tactically sidestepping industry-specific pitfalls, and sculpting bespoke investment strategies to capitalize on market fluctuations. Concurrently, individual enterprises find empowerment in aligning strategic endeavors with market trajectories, discerning key competitors, and navigating volatility with steadfast resilience. In essence, this research marks a pivotal moment in economic discourse, unveiling novel methodologies poised to redefine decision-making paradigms and elevate performance benchmarks for both investment entities and individual enterprises navigating the intricate tapestry of financial realms.
ImageInWords: Unlocking Hyper-Detailed Image Descriptions
Authors: Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, Radu Soricut
Date: 2024-05-05 00:00:00
Tasks: Image Generation, Specificity, Text to Image Generation, Text-to-Image Generation

Despite the longstanding adage "an image is worth a thousand words," generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with ImageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66%) and GPT4V (+48%) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31% against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6% on ARO, SVO-Probes, and Winoground datasets. We release the IIW Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model.
Prediction techniques for dynamic imaging with online primal-dual methods
Authors: Neil Dizon, Jyrki Jauhiainen, Tuomo Valkonen
Date: 2024-05-03 00:00:00

Online optimisation facilitates the solution of dynamic inverse problems, such as image stabilisation, fluid flow monitoring, and dynamic medical imaging. In this paper, we improve upon previous work on predictive online primal-dual methods on two fronts. Firstly, we provide a more concise analysis that symmetrises previously unsymmetric regret bounds, and relaxes previous restrictive conditions on the dual predictor. Secondly, based on the latter, we develop several improved dual predictors. We numerically demonstrate their efficacy in image stabilisation and dynamic positron emission tomography.