pwc-archive/papers-with-abstracts huggingface dataset:
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
Authors: Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein
Date: 2024-12-04 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The integration of Large Language Models (LLMs) with evolutionary computation (EC) has introduced a promising paradigm for automating the design of metaheuristic algorithms. However, existing frameworks, such as the Large Language Model Evolutionary Algorithm (LLaMEA), often lack precise control over mutation mechanisms, leading to inefficiencies in solution space exploration and potentially suboptimal convergence. This paper introduces a novel approach to mutation control within LLM-driven evolutionary frameworks, inspired by theory of genetic algorithms. Specifically, we propose dynamic mutation prompts that adaptively regulate mutation rates, leveraging a heavy-tailed power-law distribution to balance exploration and exploitation. Experiments using GPT-3.5-turbo and GPT-4o models demonstrate that GPT-3.5-turbo fails to adhere to the specific mutation instructions, while GPT-4o is able to adapt its mutation based on the prompt engineered dynamic prompts. Further experiments show that the introduction of these dynamic rates can improve the convergence speed and adaptability of LLaMEA, when using GPT-4o. This work sets the starting point for better controlled LLM-based mutations in code optimization tasks, paving the way for further advancements in automated metaheuristic design.
Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware
Authors: Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank, Eric Müller, Laurent Schmalen, Johannes Schemmel
Date: 2024-12-04 00:00:00

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.
Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware
Authors: Philipp Spilger, Eric Müller, Johannes Schemmel
Date: 2024-12-04 00:00:00

The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challenge by supporting "hybrid" plasticity, which combines the concepts of programmability and inherently parallel emulation. In particular, observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits. The evaluation of the observables, the decision to perform an update, and the magnitude of an update, are all conducted in a conventional program that runs simultaneously with the analog neural network. Consequently, these systems can offer a scalable and flexible solution in such cases. While previous work on the platform has already reported on the use of different kinds of plasticity, the descriptions for the spiking neural network experiment topology and protocol, and the plasticity algorithm have not been connected. In this work, we introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language for the BrainScaleS-2 platform and demonstrate its use.
A Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems
Authors: Zhiqiu Chen, Zong-Gan Chen, Yuncheng Jiang, Zhi-Hui Zhan
Date: 2024-12-04 00:00:00
Tasks: feature selection, Multiobjective Optimization

In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019 IEEE Congress on Evolutionary Computation test suite (CEC 2019), and their performance on real-world problems is neglected. In this paper, two types of real-world multimodal multiobjective optimization problems in feature selection and location selection respectively are formulated. Moreover, four real-world datasets of Guangzhou, China are constructed for location selection. An investigation is conducted to evaluate the performance of seven existing MMOAs in solving these two types of real-world problems. An analysis of the experimental results explores the characteristics of the tested MMOAs, providing insights for selecting suitable MMOAs in real-world applications.
Reconstruction of dynamic systems using genetic algorithms with dynamic search limits
Authors: Omar Rodríguez-Abreo, José Luis Aragón, Mario Alan Quiroz-Juárez
Date: 2024-12-03 00:00:00

Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system's dynamics from experimental data remains a significant challenge without a definitive solution. In this study, evolutionary computing techniques are presented to estimate the governing equations of a dynamical system using time-series data. The main approach is to propose polynomial equations with unknown coefficients, and subsequently perform a parametric estimation using genetic algorithms. Some of the main contributions of the present study are an adequate modification of the genetic algorithm to remove terms with minimal contributions, and a mechanism to escape local optima during the search. To evaluate the proposed method, we applied it to three dynamical systems: a linear model, a nonlinear model, and the Lorenz system. Our results demonstrate a reconstruction with an Integral Square Error below 0.22 and a coefficient of determination R-squared of 0.99 for all systems, indicating successful reconstruction of the governing dynamic equations.
Were You Helpful -- Predicting Helpful Votes from Amazon Reviews
Authors: Emin Kirimlioglu, Harrison Kung, Dominic Orlando
Date: 2024-12-03 00:00:00
Tasks: Sentiment Analysis

This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. After extensive feature analysis and correlation testing, we identified key metadata characteristics that serve as strong predictors of review helpfulness. While we initially explored natural language processing approaches using TextBlob for sentiment analysis, our final model focuses on metadata features that demonstrated more significant correlations, including the number of images per review, reviewer's historical helpful votes, and temporal aspects of the review. The data pipeline encompasses careful preprocessing and feature standardization steps to prepare the input for model training. Through systematic evaluation of different feature combinations, we discovered that metadata elements we choose using a threshold provide reliable signals when combined for predicting how helpful other Amazon users will find a review. This insight suggests that contextual and user-behavioral factors may be more indicative of review helpfulness than the linguistic content itself.
Fine Tuning Swimming Locomotion Learned from Mosquito Larvae
Authors: Pranav Rajbhandari, Karthick Dhileep, Sridhar Ravi, Donald Sofge
Date: 2024-11-16 00:00:00

In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this copied solution for the swimmer model. We utilize Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the CFD model, we additionally train a deep learning model to replicate the forces acting on the swimmer model. We find that this method is effective at performing local search to improve the parameterized swimming locomotion.
Bounds in Wasserstein distance for locally stationary processes
Authors: Jan Nino G. Tinio, Mokhtar Z. Alaya, Salim Bouzebda
Date: 2024-12-04 00:00:00

Locally stationary processes (LSPs) provide a robust framework for modeling time-varying phenomena, allowing for smooth variations in statistical properties such as mean and variance over time. In this paper, we address the estimation of the conditional probability distribution of LSPs using Nadaraya-Watson (NW) type estimators. The NW estimator approximates the conditional distribution of a target variable given covariates through kernel smoothing techniques. We establish the convergence rate of the NW conditional probability estimator for LSPs in the univariate setting under the Wasserstein distance and extend this analysis to the multivariate case using the sliced Wasserstein distance. Theoretical results are supported by numerical experiments on both synthetic and real-world datasets, demonstrating the practical usefulness of the proposed estimators.
Beyond Questions: Leveraging ColBERT for Keyphrase Search
Authors: Jorge Gabín, Javier Parapar, Craig Macdonald
Date: 2024-12-04 00:00:00
Tasks: Document Ranking, Retrieval

While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This query type is also prevalent in specialised search tasks such as academic or professional search, where experts rely on keyphrases to articulate their information needs. However, current dense retrieval models often fail with keyphrase-like queries, primarily because they are mostly trained on question-like ones. This paper introduces a novel model that employs the ColBERT architecture to enhance document ranking for keyphrase queries. For that, given the lack of large keyphrase-based retrieval datasets, we first explore how Large Language Models can convert question-like queries into keyphrase format. Then, using those keyphrases, we train a keyphrase-based ColBERT ranker (ColBERTKP_QD) to improve the performance when working with keyphrase queries. Furthermore, to reduce the training costs associated with training the full ColBERT model, we investigate the feasibility of training only a keyphrase query encoder while keeping the document encoder weights static (ColBERTKP_Q). We assess our proposals' ranking performance using both automatically generated and manually annotated keyphrases. Our results reveal the potential of the late interaction architecture when working under the keyphrase search scenario.
Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
Authors: Menghan Wang, Yuchen Guo, Duanfeng Zhang, Jianian Jin, Minnie Li, Dan Schonfeld, Shawn Zhou
Date: 2024-11-17 00:00:00
Tasks: Explainable Recommendation, Hallucination

How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts to LLM, and then map LLM response to real enterprise products. To mitigate the risks associated with LLM hallucination, we employ rigorous evaluation and pruning methods to ensure the reliability and availability of the KG. Through an A/B test conducted on an e-commerce website, we demonstrate the effectiveness of LLM-PKG in driving user engagements and transactions significantly.
Monolithic Hybrid Recommender System for Suggesting Relevant Movies
Authors: Mahdi Rezapour
Date: 2024-11-16 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommendation systems have become the fundamental services to facilitate users information access. Generally, recommendation system works by filtering historical behaviors to understand and learn users preferences. With the growth of online information, recommendations have become of crucial importance in information filtering to prevent the information overload problem. In this study, we considered hybrid post-fusion of two approaches of collaborative filtering, by using sequences of watched movies and considering the related movies rating. After considering both techniques and applying the weights matrix, the recommendations would be modified to correspond to the users preference as needed. We discussed that various weights would be set based on use cases. For instance, in cases where we have the rating for most classes, we will assign a higher weight to the rating matrix and in case where the rating is unavailable for the majority of cases, the higher weights might be assigned to the sequential dataset. An extensive discussion is made in the context of this paper. Sequential type of the watched movies was used in conjunction of the rating as especially that model might be inadequate in distinguishing users long-term preference and that does not account for the rating of the watched movies and thus that model along might not suffice. Extensive discussion was made regarding the literature and methodological approach to solve the problem.
Markov Constraint as Large Language Model Surrogate
Authors: Alexandre Bonlarron, Jean-Charles Régin
Date: 2024-06-11 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Sentence, Text Generation

This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP). It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence. The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions. It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram). The experimental results show that the generated text is valued in a similar way to the LLM perplexity function. Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams.
MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation
Authors: Arkadiusz Modzelewski, Giovanni Da San Martino, Pavel Savov, Magdalena Anna Wilczyńska, Adam Wierzbicki
Date: 2024-11-11 00:00:00
Proceeding: Empirical Methods in Natural Language Processing (EMNLP) 2024 11
Tasks: Articles, Classification

This study presents a novel corpus of 15,356 Polish web articles, including articles identified as containing disinformation. Our dataset enables a multifaceted understanding of disinformation. We present a distinctive multilayered methodology for annotating disinformation in texts. What sets our corpus apart is its focus on uncovering hidden intent and manipulation in disinformative content. A team of experts annotated each article with multiple labels indicating both disinformation creators’ intents and the manipulation techniques employed. Additionally, we set new baselines for binary disinformation detection and two multiclass multilabel classification tasks: manipulation techniques and intention types classification.
Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets
Authors: Shir Bar, Or Hirschorn, Roi Holzman, Shai Avidan
Date: 2024-12-04 00:00:00
Tasks: Anomaly Detection

In the study of animal behavior, researchers often record long continuous videos, accumulating into large-scale datasets. However, the behaviors of interest are often rare compared to routine behaviors. This incurs a heavy cost on manual annotation, forcing users to sift through many samples before finding their needles. We propose a pipeline to efficiently sample rare behaviors from large datasets, enabling the creation of training datasets for rare behavior classifiers. Our method only needs an unlabeled animal pose or acceleration dataset as input and makes no assumptions regarding the type, number, or characteristics of the rare behaviors. Our pipeline is based on a recent graph-based anomaly detection model for human behavior, which we apply to this new data domain. It leverages anomaly scores to automatically label normal samples while directing human annotation efforts toward anomalies. In research data, anomalies may come from many different sources (e.g., signal noise versus true rare instances). Hence, the entire labeling budget is focused on the abnormal classes, letting the user review and label samples according to their needs. We tested our approach on three datasets of freely-moving animals, acquired in the laboratory and the field. We found that graph-based models are particularly useful when studying motion-based behaviors in animals, yielding good results while using a small labeling budget. Our method consistently outperformed traditional random sampling, offering an average improvement of 70% in performance and creating datasets even when the behavior of interest was only 0.02% of the data. Even when the performance gain was minor (e.g., when the behavior is not rare), our method still reduced the annotation effort by half.
LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting
Authors: Hubert Truchan, Christian Kalfar, Zahra Ahmadi
Date: 2024-10-21 00:00:00
Proceeding: CIKM 2024 10
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

The progress of deep-learning-based forecasting architectures is evident through their expanding parameter configurations. However, the need for rapid online decision making in practical scenarios calls for an alternative strategy, highlighting the necessity for networks that are not only adaptive but also efficient in real-time operations. This shift is critical as we confront three principal challenges in deep-learning-based forecasting frameworks: (i) the inherent limitations of transformers, which, despite their attempts to preserve ordering information, the temporal information loss due to the permutation-invariant nature of self-attention mechanisms is inevitable, (ii) the inefficacy of linear models in capturing the dynamic interactions within swiftly evolving signals; and (iii) the incapacity of tree-based approaches to extrapolating beyond values present in the training set. In response to these challenges, we introduce LTBoost, an innovative boosted hybrid of linear and tree-based ensemble gradient algorithms tailored for long-term time series forecasting (LTSF) tasks,  scalable to high data dimensions. LTBoost employs a dual strategy, beginning with a linear regression model to capture trends and extrapolate beyond known data, complemented by a robust nonlinear tree-based model that focuses on the residuals. This boosted hybrid approach not only addresses the challenges posed by existing models but also significantly improves forecast accuracy. The effectiveness of LTBoost is validated through empirical experiments conducted on nine well-established benchmark datasets, demonstrating superior performance and achieving state-of-the-art results in 32 out of 36 cases, measured by mean absolute error (MAE). Our findings also explore the impact of lag features and signal normalization techniques, demonstrating further improvements in predictive accuracy. This hybrid and highly effective approach highlights LTBoost's innovation and its resolution of specific forecasting challenges, setting the stage for its contribution to the field of time series forecasting,  paving the way for its application in diverse real-world scenarios.
Enrico Thanhoffer
Authors: Enrico Thanhoffer
Date: 2023-10-19 00:00:00
Proceeding: 12/5 2024 12
Tasks: Misinformation

Enrico Thanhoffer is an Italian artist and architect known for his innovative artistic m.ovement called Globism. This technique combines figurative and abstract elements, using the computer as the main tool for creating the works. Thanhoffer developed Globism to challenge traditional categorizations of art, seeking to explore the fundamental truths of existence through his digital art.
The works address pressing issues such as social justice, environmental conservation and the impact of globalization. For example, in works such as "Fraudulent Doctors" he criticizes misinformation and fake news, while "City of Tomorrow" explores the negative consequences of urbanization.
His statement that "reality is appearance, but appearance is not reality" summarizes his philosophical approach to art and creativity
Research on Financial Multi-Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing
Authors: Fu Lei, Ge Shi
Date: 2024-12-04 00:00:00
Tasks: Management, Prediction, Time Series

In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of risk prediction. Through empirical analysis of data from multiple asset classes such as stocks, bonds, commodities, and foreign exchange, the results show that the proposed CNN model significantly outperforms traditional models in terms of prediction accuracy and robustness, especially under extreme market conditions. This research provides a new method for financial risk management, with important theoretical significance and practical value.