pwc-archive/papers-with-abstracts huggingface dataset:
ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics
Authors: Zherui Zhang, Fan Yang, Ran Cheng, Yuxin Ma
Date: 2024-08-08 00:00:00
Tasks: Computational Efficiency, Evolutionary Algorithms

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful tools for solving complex optimization problems characterized by multiple, often conflicting, objectives. While advancements have been made in computational efficiency as well as diversity and convergence of solutions, a critical challenge persists: the internal evolutionary mechanisms are opaque to human users. Drawing upon the successes of explainable AI in explaining complex algorithms and models, we argue that the need to understand the underlying evolutionary operators and population dynamics within MOEAs aligns well with a visual analytics paradigm. This paper introduces ParetoTracker, a visual analytics framework designed to support the comprehension and inspection of population dynamics in the evolutionary processes of MOEAs. Informed by preliminary literature review and expert interviews, the framework establishes a multi-level analysis scheme, which caters to user engagement and exploration ranging from examining overall trends in performance metrics to conducting fine-grained inspections of evolutionary operations. In contrast to conventional practices that require manual plotting of solutions for each generation, ParetoTracker facilitates the examination of temporal trends and dynamics across consecutive generations in an integrated visual interface. The effectiveness of the framework is demonstrated through case studies and expert interviews focused on widely adopted benchmark optimization problems.
Sparse Spiking Neural-like Membrane Systems on Graphics Processing Units
Authors: Javier Hernández-Tello, Miguel Ángel Martínez-del-Amor, David Orellana-Martín, Francis George C. Cabarle
Date: 2024-08-08 00:00:00
Tasks: GPU

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.
Theoretical Advantage of Multiobjective Evolutionary Algorithms for Problems with Different Degrees of Conflict
Authors: Weijie Zheng
Date: 2024-08-08 00:00:00
Tasks: Evolutionary Algorithms

The field of multiobjective evolutionary algorithms (MOEAs) often emphasizes its popularity for optimization problems with conflicting objectives. However, it is still theoretically unknown how MOEAs perform for different degrees of conflict, even for no conflicts, compared with typical approaches outside this field. As the first step to tackle this question, we propose the OneMaxMin$_k$ benchmark class with the degree of the conflict $k\in[0..n]$, a generalized variant of COCZ and OneMinMax. Two typical non-MOEA approaches, scalarization (weighted-sum approach) and $\epsilon$-constraint approach, are considered. We prove that for any set of weights, the set of optima found by scalarization approach cannot cover the full Pareto front. Although the set of the optima of constrained problems constructed via $\epsilon$-constraint approach can cover the full Pareto front, the general used ways (via exterior or nonparameter penalty functions) to solve such constrained problems encountered difficulties. The nonparameter penalty function way cannot construct the set of optima whose function values are the Pareto front, and the exterior way helps (with expected runtime of $O(n\ln n)$ for the randomized local search algorithm for reaching any Pareto front point) but with careful settings of $\epsilon$ and $r$ ($r>1/(\epsilon+1-\lceil \epsilon \rceil)$). In constrast, the generally analyzed MOEAs can efficiently solve OneMaxMin$_k$ without above careful designs. We prove that (G)SEMO, MOEA/D, NSGA-II, and SMS-EMOA can cover the full Pareto front in $O(\max\{k,1\}n\ln n)$ expected number of function evaluations, which is the same asymptotic runtime as the exterior way in $\epsilon$-constraint approach with careful settings. As a side result, our results also give the performance analysis of solving a constrained problem via multiobjective way.
A tutorial on the dynamic Laplacian
Authors: Gary Froyland
Date: 2024-08-08 00:00:00

Spectral techniques are popular and robust approaches to data analysis. A prominent example is the use of eigenvectors of a Laplacian, constructed from data affinities, to identify natural data groupings or clusters, or to produce a simplified representation of data lying on a manifold. This tutorial concerns the dynamic Laplacian, which is a natural generalisation of the Laplacian to handle data that has a time component and lies on a time-evolving manifold. In this dynamic setting, clusters correspond to long-lived ``coherent'' collections. We begin with a gentle recap of spectral geometry before describing the dynamic generalisations. We also discuss computational methods and the automatic separation of many distinct features through the SEBA algorithm. The purpose of this tutorial is to bring together many results from the dynamic Laplacian literature into a single short document, written in an accessible style.
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Authors: Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Date: 2024-08-08 00:00:00
Tasks: Retrieval, Text Retrieval

Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.
Enhanced Semantic Graph Based Approach With Sentiment Analysis For User Interest Retrieval From Social Sites
Authors: Usama Ahmed Jamal
Date: 2024-08-08 00:00:00
Tasks: Marketing, Recommendation Systems, Retrieval, Sentiment Analysis

Blogs and social networking sites serve as a platform to the users for expressing their interests, ideas and thoughts. Targeted marketing uses the recommendation systems for suggesting their services and products to the users or clients. So the method used by target marketing is extraction of keywords and main topics from the user generated texts. Most of conventional methods involve identifying the personal interests just on the basis of surveys and rating systems. But the proposed research differs in manner that it aim at using the user generated text as a source medium for identifying and analyzing the personal interest as a knowledge base area of users. Semantic graph based approach is proposed research work that identifies the references of clients and users by analyzing their own texts such as tweets. The keywords need to be extracted from the text generated by the user on the social networking sites. This can be made possible by using several algorithms that extracts the keywords automatically from the available content provided by the user. Based on frequency and degree it ranks the extracted keywords. Furthermore, semantic graph based model assists in providing useful suggestions just by extracting the interests of users by analyzing their contents from social media. In this approach graph comprises of nodes and edges where nodes represents the keywords extracted by the algorithm and edges shows the semantic connection between the nodes. The method does not require internet related user activities like surveys or ratings to gather user interest related information.
Understanding and Modeling Job Marketplace with Pretrained Language Models
Authors: Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li
Date: 2024-08-08 00:00:00
Tasks: Graph Neural Network

Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job seekers and employers, ultimately contributing to the greater good of the society. However, existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. To address the above challenges, we propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph, aiming to fully utilize the pretrained knowledge and reasoning ability to model member/job textual features as well as various member-job relations simultaneously. In the pretraining phase, we propose a heterogeneous ego-graph-based prompting strategy to model and aggregate member/job textual features based on the topological structure around the target member/job node, where entity type embeddings and graph positional embeddings are introduced accordingly to model different entities and their heterogeneous relations. Meanwhile, a proximity-aware attention alignment strategy is designed to dynamically adjust the attention of the PLM on ego-graph node tokens in the prompt, such that the attention can be better aligned with job marketplace semantics. Extensive experiments at LinkedIn demonstrate the effectiveness of PLM4Job.
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Authors: Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Date: 2024-08-08 00:00:00
Tasks: Exposure Fairness, Fairness, Learning-To-Rank, Multi-Armed Bandits, Recommendation Systems

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in recommendation lists, leading to a feedback loop that further amplifies this bias. Although extensive research has addressed this issue in model-based or neighborhood-based recommendation algorithms, less attention has been paid to online recommendation models, such as those based on top-K contextual bandits, where recommendation models are dynamically updated with ongoing user feedback. In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits. We analyze these algorithms in their ability to handle exposure bias and provide a fair representation of items in the recommendation results. Our analysis reveals that these algorithms fail to mitigate exposure bias in the long run during the course of ongoing user interactions. We propose an Exposure-Aware reward model that updates the model parameters based on two factors: 1) implicit user feedback and 2) the position of the item in the recommendation list. The proposed model mitigates exposure bias by controlling the utility assigned to the items based on their exposure in the recommendation list. Our experiments with two real-world datasets show that our proposed reward model improves the exposure fairness of the linear cascading bandits over time while maintaining the recommendation accuracy. It also outperforms the current baselines. Finally, we prove a high probability upper regret bound for our proposed model, providing theoretical guarantees for its performance.
Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth
Authors: Richard D. Paul, Johannes Seiffarth, Hanno Scharr, Katharina Nöh
Date: 2024-08-08 00:00:00
Tasks: Cell Tracking, Instance Segmentation, Semantic Segmentation

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.
Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Authors: Richárd Kiss, Gábor Szűcs
Date: 2024-07-12 00:00:00
Proceeding: Complex & Intelligent Systems 2024 7
Tasks: Graph Representation Learning, Inductive Learning, Node Classification, Representation Learning

Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications like social network analysis and biological modeling. While shallow embedding algorithms excel at capturing network structure, they face a critical limitation – failing to generalize to unseen nodes. This paper addresses this challenge by introducing Inductive Shallow Node Embedding – as a main contribution – pioneering a novel approach that extends shallow embeddings to the realm of inductive learning. It has a novel encoder architecture that captures the local neighborhood structure of each node, enabling effective generalization to unseen nodes. In the generalization, robustness is essential to avoid degradation of performance arising from noise in the dataset. It has been theoretically proven that the covariance of the additive noise term in the proposed model is inversely proportional to the cardinality of a node’s neighbors. Another contribution is a mathematical lower bound to quantify the robustness of node embeddings, confirming its advantage over traditional shallow embedding methods, particularly in the presence of parameter noise. The proposed method demonstrably excels in dynamic networks, consistently achieving over 90% performance on previously unseen nodes compared to nodes encountered during training on various benchmarks. The empirical evaluation concludes that our method outperforms competing methods on the vast majority of datasets in both transductive and inductive tasks.
The Struggles of LLMs in Cross-lingual Code Clone Detection
Authors: Micheline Bénédicte Moumoula, Abdoul Kader Kabore, Jacques Klein, Tegawendé Bissyande
Date: 2024-08-08 00:00:00
Tasks: Clone Detection

With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering community. Numerous studies have explored this topic, proposing various promising approaches. Inspired by the significant advances in machine learning in recent years, particularly Large Language Models (LLMs), which have demonstrated their ability to tackle various tasks, this paper revisits cross-lingual code clone detection. We evaluate the performance of five (05) LLMs and eight prompts (08) for the identification of cross-lingual code clones. Additionally, we compare these results against two baseline methods. Finally, we evaluate a pre-trained embedding model to assess the effectiveness of the generated representations for classifying clone and non-clone pairs. The studies involving LLMs and Embedding models are evaluated using two widely used cross-lingual datasets, XLCoST and CodeNet. Our results show that LLMs can achieve high F1 scores, up to 0.99, for straightforward programming examples. However, they not only perform less well on programs associated with complex programming challenges but also do not necessarily understand the meaning of "code clones" in a cross-lingual setting. We show that embedding models used to represent code fragments from different programming languages in the same representation space enable the training of a basic classifier that outperforms all LLMs by ~1 and ~20 percentage points on the XLCoST and CodeNet datasets, respectively. This finding suggests that, despite the apparent capabilities of LLMs, embeddings provided by embedding models offer suitable representations to achieve state-of-the-art performance in cross-lingual code clone detection.
Reinforcement Learning Based Escape Route Generation in Low Visibility Environments
Authors: Hari Srikanth
Date: 2024-05-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Structure fires are responsible for the majority of fire-related deaths nationwide. In order to assist with the rapid evacuation of trapped people, this paper proposes the use of a system that determines optimal search paths for firefighters and exit paths for civilians in real time based on environmental measurements. Through the use of a LiDAR mapping system evaluated and verified by a trust range derived from sonar and smoke concentration data, a proposed solution to low visibility mapping is tested. These independent point clouds are then used to create distinct maps, which are merged through the use of a RANSAC based alignment methodology and simplified into a visibility graph. Temperature and humidity data are then used to label each node with a danger score, creating an environment tensor. After demonstrating how a Linear Function Approximation based Natural Policy Gradient RL methodology outperforms more complex competitors with respect to robustness and speed, this paper outlines two systems (savior and refugee) that process the environment tensor to create safe rescue and escape routes, respectively.
BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation
Authors: Akanksha Karotia, Seba Susan
Date: 2024-08-16 00:00:00
Proceeding: The 23rd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks 2024 8
Tasks: Abstractive Text Summarization, Articles, Domain Adaptation, Lay Summarization

Lay summarization is essential but challenging, as it simplifies scientific information for non-experts and keeps them updated with the latest scientific knowledge. In our participation in the Shared Task: Lay Summarization of Biomedical Research Articles @ BioNLP Workshop (Goldsack et al., 2024), ACL 2024, we conducted a comprehensive evaluation on abstractive summarization of biomedical literature using Large Language Models (LLMs) and assessed the performance using ten metrics across three categories: relevance, readability, and factuality, using eLife and PLOS datasets provided by the organizers. We developed a two-stage framework for lay summarization of biomedical scientific articles. In the first stage, we generated summaries using BART and PEGASUS LLMs by fine-tuning them on the given datasets. In the second stage, we combined the generated summaries and input them to BioBART, and then fine-tuned it on the same datasets. Our findings show that combining general and domain-specific LLMs enhances performance.
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
Authors: Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà
Date: 2024-08-10 00:00:00
Tasks: Machine Unlearning

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certain application-specific scenarios may require fine-tuning these models on private data. This survey critically examines the privacy threats associated with LLMs, emphasizing the potential for these models to memorize and inadvertently reveal sensitive information. We explore current threats by reviewing privacy attacks on LLMs and propose comprehensive solutions for integrating privacy mechanisms throughout the entire learning pipeline. These solutions range from anonymizing training datasets to implementing differential privacy during training or inference and machine unlearning after training. Our comprehensive review of existing literature highlights ongoing challenges, available tools, and future directions for preserving privacy in LLMs. This work aims to guide the development of more secure and trustworthy AI systems by providing a thorough understanding of privacy preservation methods and their effectiveness in mitigating risks.