pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical Analyses of Evolutionary Algorithms on Time-Linkage OneMax with General Weights
Authors: Weijie Zheng, Xin Yao
Date: 2023-05-11 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary computation has shown its superiority in dynamic optimization, but for the (dynamic) time-linkage problems, some theoretical studies have revealed the possible weakness of evolutionary computation. Since the theoretically analyzed time-linkage problem only considers the influence of an extremely strong negative time-linkage effect, it remains unclear whether the weakness also appears in problems with more general time-linkage effects. Besides, understanding in depth the relationship between time-linkage effect and algorithmic features is important to build up our knowledge of what algorithmic features are good at what kinds of problems. In this paper, we analyze the general time-linkage effect and consider the time-linkage OneMax with general weights whose absolute values reflect the strength and whose sign reflects the positive or negative influence. We prove that except for some small and positive time-linkage effects (that is, for weights $0$ and $1$), randomized local search (RLS) and (1+1)EA cannot converge to the global optimum with a positive probability. More precisely, for the negative time-linkage effect (for negative weights), both algorithms cannot efficiently reach the global optimum and the probability of failing to converge to the global optimum is at least $1-o(1)$. For the not so small positive time-linkage effect (positive weights greater than $1$), such a probability is at most $c+o(1)$ where $c$ is a constant strictly less than $1$.
On the Partial Convexification for Low-Rank Spectral Optimization: Rank Bounds and Algorithms
Authors: Yongchun Li, Weijun Xie
Date: 2023-05-12 00:00:00

A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective subject to multiple two-sided linear matrix inequalities intersected with a low-rank and spectral constrained domain set. Although solving LSOP is, in general, NP-hard, its partial convexification (i.e., replacing the domain set by its convex hull) termed "LSOP-R," is often tractable and yields a high-quality solution. This motivates us to study the strength of LSOP-R. Specifically, we derive rank bounds for any extreme point of the feasible set of LSOP-R and prove their tightness for the domain sets with different matrix spaces. The proposed rank bounds recover two well-known results in the literature from a fresh angle and also allow us to derive sufficient conditions under which the relaxation LSOP-R is equivalent to the original LSOP. To effectively solve LSOP-R, we develop a column generation algorithm with a vector-based convex pricing oracle, coupled with a rank-reduction algorithm, which ensures the output solution satisfies the theoretical rank bound. Finally, we numerically verify the strength of the LSOP-R and the efficacy of the proposed algorithms.
Hierarchically Coherent Multivariate Mixture Networks
Authors: Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski
Date: 2023-05-11 00:00:00
Tasks: Computational Efficiency, Time Series

Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic coherent forecasting is tasked to produce forecasts consistent across levels of aggregation. In this study, we propose to augment neural forecasting architectures with a coherent multivariate mixture output. We optimize the networks with a composite likelihood objective, allowing us to capture time series' relationships while maintaining high computational efficiency. Our approach demonstrates 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines. We conduct ablation studies of the framework components and provide theoretical foundations for them. To assist related work, the code is available at this https://github.com/Nixtla/neuralforecast.
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
Authors: Santiago de Leon-Martinez, Robert Moro, Maria Bielikova
Date: 2023-05-12 00:00:00
Tasks: Collaborative Filtering, Movie Recommendation, Recommendation Systems

Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Authors: Iain Mackie, Shubham Chatterjee, Jeffrey Dalton
Date: 2023-05-12 00:00:00
Tasks: Document Ranking, Retrieval

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectiveness. This work extends GRF to dense and learned sparse retrieval paradigms with experiments over six standard document ranking benchmarks. We find that GRF improves over comparable PRF techniques by around 10% on both precision and recall-oriented measures. Nonetheless, query analysis shows that GRF and PRF have contrasting benefits, with GRF providing external context not present in first-pass retrieval, whereas PRF grounds the query to the information contained within the target corpus. Thus, we propose combining generative and pseudo-relevance feedback ranking signals to achieve the benefits of both feedback classes, which significantly increases recall over PRF methods on 95% of experiments.
Knowledge Soft Integration for Multimodal Recommendation
Authors: Kai Ouyang, Chen Tang, Wenhao Zheng, Xiangjin Xie, Xuanji Xiao, Jian Dong, Hai-Tao Zheng, Zhi Wang
Date: 2023-05-12 00:00:00
Tasks: Graph Neural Network, Multimodal Recommendation, Recommendation Systems, Retrieval

One of the main challenges in modern recommendation systems is how to effectively utilize multimodal content to achieve more personalized recommendations. Despite various proposed solutions, most of them overlook the mismatch between the knowledge gained from independent feature extraction processes and downstream recommendation tasks. Specifically, multimodal feature extraction processes do not incorporate prior knowledge relevant to recommendation tasks, while recommendation tasks often directly use these multimodal features as side information. This mismatch can lead to model fitting biases and performance degradation, which this paper refers to as the \textit{curse of knowledge} problem. To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about. To achieve this, we put forward a Knowledge Soft Integration framework for the multimodal recommendation, abbreviated as KSI, which is composed of the Structure Efficiently Injection (SEI) module and the Semantic Soft Integration (SSI) module. In the SEI module, we model the modality correlation between items using Refined Graph Neural Network (RGNN), and introduce a regularization term to reduce the redundancy of user/item representations. In the SSI module, we design a self-supervised retrieval task to further indirectly integrate the semantic knowledge of multimodal features, and enhance the semantic discrimination of item representations. Extensive experiments on three benchmark datasets demonstrate the superiority of KSI and validate the effectiveness of its two modules.
Music Rearrangement Using Hierarchical Segmentation
Authors: Christos Plachouras, Marius Miron
Date: 2023-05-12 00:00:00
Tasks: Segmentation

Music rearrangement involves reshuffling, deleting, and repeating sections of a music piece with the goal of producing a standalone version that has a different duration. It is a creative and time-consuming task commonly performed by an expert music engineer. In this paper, we propose a method for automatically rearranging music recordings that takes into account the hierarchical structure of the recording. Previous approaches focus solely on identifying cut-points in the audio that could result in smooth transitions. We instead utilize deep audio representations to hierarchically segment the piece and define a cut-point search subject to the boundaries and musical functions of the segments. We score suitable entry- and exit-point pairs based on their similarity and the segments they belong to, and define an optimal path search. Experimental results demonstrate the selected cut-points are most commonly imperceptible by listeners and result in more consistent musical development with less distracting repetitions.
Methods and Tools to Advance the Retrieval of Mathematical Knowledge from Digital Libraries for Search-, Recommendation-, and Assistance-Systems
Authors: Bela Gipp, André Greiner-Petter, Moritz Schubotz, Norman Meuschke
Date: 2023-05-12 00:00:00
Tasks: Information Retrieval, Retrieval

This project investigated new approaches and technologies to enhance the accessibility of mathematical content and its semantic information for a broad range of information retrieval applications. To achieve this goal, the project addressed three main research challenges: (1) syntactic analysis of mathematical expressions, (2) semantic enrichment of mathematical expressions, and (3) evaluation using quality metrics and demonstrators. To make our research useful for the research community, we published tools that enable researchers to process mathematical expressions more effectively and efficiently.
Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide
Authors: Deven Santosh Shah, Gosuddin Kamaruddin Siddiqi, Shiying He, Radhika Bansal
Date: 2023-05-11 00:00:00
Tasks: Articles, Misinformation, News Recommendation, Recommendation Systems

Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence national audiences as some local stories may have wider implications for politics, environment or crime. Hence, detecting the exact geolocation and impact scope of local news is crucial for news recommendation systems. There are two fundamental things required in this process, (1) classify whether an article belongs to local news, and (2) identify the geolocation of the article and its scope of influence to recommend it to appropriate users. In this paper, we focus on the second step and propose (1) an efficient approach to determine the location and radius of local news articles, (2) a method to reconcile the user's location with the article's location, and (3) a metric to evaluate the quality of the local news feed. We demonstrate that our technique is scalable and effective in serving hyperlocal news to users worldwide.
Automated Data Denoising for Recommendation
Authors: Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane, Jose Sepulveda, James Caverlee, Fei Wang
Date: 2023-05-11 00:00:00
Tasks: Denoising, Recommendation Systems

In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data sparsity, implicit feedback is often the default choice for training recommender systems (RS), however, such data could be very noisy due to the randomness and diversity of user behaviors. For instance, a large portion of clicks may not reflect true user preferences and many purchases may result in negative reviews or returns. Fortunately, by utilizing the strengths of both types of feedback to compensate for the weaknesses of the other, we can mitigate the above issue at almost no cost. In this work, we propose an Automated Data Denoising framework, \textbf{\textit{AutoDenoise}}, for recommendation, which uses a small number of explicit data as validation set to guide the recommender training. Inspired by the generalized definition of curriculum learning (CL), AutoDenoise learns to automatically and dynamically assign the most appropriate (discrete or continuous) weights to each implicit data sample along the training process under the guidance of the validation performance. Specifically, we use a delicately designed controller network to generate the weights, combine the weights with the loss of each input data to train the recommender system, and optimize the controller with reinforcement learning to maximize the expected accuracy of the trained RS on the noise-free validation set. Thorough experiments indicate that AutoDenoise is able to boost the performance of the state-of-the-art recommendation algorithms on several public benchmark datasets.
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text
Authors: Krishanu Das Baksi, Vatsala Pokhrel, Kuntal Kumar Bhusan, Sharmila Mande
Date: 2023-04-27 00:00:00
Tasks: Transfer Learning

The community of different types of microbes present in a biological niche plays a very important role in functioning of the system. The crosstalk or interactions among the different microbes contributes to the building blocks of such microbial community structures. Evidence reported in biomedical text serves as a reliable source for predicting such interactions. However, going through the vast and ever-increasing volume of biomedical literature is an intimidating and time consuming process. This necessitates development of automated methods capable of accurately extracting bacterial relations reported in biomedical literature. In this paper, we introduce a method for automated extraction of microbial interactions (specifically between bacteria) from biomedical literature along with ways of using transfer learning to improve its accuracy. We also describe a pipeline using which relations among specific bacteria groups can be mined. Additionally, we introduce the first publicly available dataset which can be used to develop bacterial interaction extraction methods.
Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs
Authors: Fethi Ali Cheddad
Date: 2023-05-11 00:00:00

This field case study aims to address the challenge of accurately predicting petrophysical properties in heterogeneous reservoir formations, which can significantly impact reservoir performance predictions. The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predict permeability log from conventional logs and match it with core data. The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The study utilized the Flow Zone Indicator (FZI) rock typing technique to understand the factors influencing reservoir quality. The findings will be used to improve reservoir simulation and locate future wells more accurately. The study concluded that the FZI approach and machine learning algorithms are effective in predicting permeability log and improving reservoir performance predictions.
Privacy-Preserving Adaptive Traffic Signal Control in a Connected Vehicle Environment
Authors: Chaopeng Tan, Kaidi Yang
Date: 2023-05-12 00:00:00
Tasks: Privacy Preserving, Traffic Signal Control

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy-preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using CV data. Specifically, we leverage secure Multi-Party Computation and differential privacy to devise a privacy-preserving CV data aggregation mechanism, which can calculate key traffic quantities without any CVs having to reveal their private data. We further develop a linear optimization model for adaptive signal control based on the traffic variables obtained via the data aggregation mechanism. The proposed linear programming problem is further extended to a stochastic programming problem to explicitly handle the noises added by the differentially private mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Authors: Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Date: 2023-05-11 00:00:00

We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound. To model the delays, we introduce Age of Information Processes (AoIPs): stochastic processes on the non-negative integers with a unit growth property. We show that AoIPs with an arbitrary moment bound cannot exceed any fraction of time infinitely often. In combination with a suitably chosen stepsize, this property turns out to be sufficient for the stability of distributed SAs. Compared to the BMT, our analysis requires crucial modifications and a new line of argument to handle the SA errors caused by AoI. In our analysis, we show that these SA errors satisfy a recursive inequality. To evaluate this recursion, we propose a new Gronwall-type inequality for time-varying lower limits of summations. As applications to our distributed BMT, we discuss distributed gradient-based optimization and a new approach to analyzing SAs with momentum.
Learning-Augmented Online Packet Scheduling with Deadlines
Authors: Ya-Chun Liang, Clifford Stein, Hao-Ting Wei
Date: 2023-05-11 00:00:00
Tasks: Management, Prediction, Scheduling

The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to cope with the prediction. We show that when the prediction error is small, our algorithm improves the competitive ratio while still maintaining a bounded competitive ratio, regardless of the prediction error.
A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks
Authors: Mahmoud Zaher, Emil Björnson, Marina Petrova
Date: 2023-05-12 00:00:00
Tasks: Scheduling

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.
Generalized Noncoherent Space-Time Block Codes from Quantum Error Correction
Authors: S. Andrew Lanham, Eli Bradley, Brian R. La Cour
Date: 2023-05-11 00:00:00

This paper generalizes results in noncoherent space-time block code (STBC) design based on quantum error correction (QEC) to new antenna configurations. Previous work proposed QEC-inspired STBCs for antenna geometries where the number of transmit and receive antennas were equal and a power of two. In this work we extend these results by providing QEC-inspired STBCs applicable to all square antenna geometries and some rectangular geometries where the number of receive antennas is greater than the number of transmit antennas. We derive the maximum-likelihood decoding rule for this family of codes for the special case of Rayleigh fading with additive white Gaussian noise. We present Monte Carlo simulations of the performance of the codes in this environment for a three-antenna square geometry and a three-by-six rectangular geometry. We demonstrate competitive performance for these codes with respect to a popular noncoherent differential code.
Building resilient organizations: The roles of top-down vs. bottom-up organizing
Authors: Stephan Leitner
Date: 2023-05-12 00:00:00

Organizations face numerous challenges posed by unexpected events such as energy price hikes, pandemic disruptions, terrorist attacks, and natural disasters, and the factors that contribute to organizational success in dealing with such disruptions often remain unclear. This paper analyzes the roles of top-down and bottom-up organizational structures in promoting organizational resilience. To do so, an agent-based model of stylized organizations is introduced that features learning, adaptation, different modes of organizing, and environmental disruptions. The results indicate that bottom-up designed organizations tend to have a higher ability to absorb the effects of environmental disruptions, and situations are identified in which either top-down or bottom-up designed organizations have an advantage in recovering from shocks.