pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods
Authors: Luping Qu, Mauricio Araya-Polo, Laurent Demanet
Date: 2024-09-10 00:00:00
Tasks: Computational Efficiency, Seismic Inversion, Uncertainty Quantification

Seismic inversion is essential for geophysical exploration and geological assessment, but it is inherently subject to significant uncertainty. This uncertainty stems primarily from the limited information provided by observed seismic data, which is largely a result of constraints in data collection geometry. As a result, multiple plausible velocity models can often explain the same set of seismic observations. In deep learning-based seismic inversion, uncertainty arises from various sources, including data noise, neural network design and training, and inherent data limitations. This study introduces a novel approach to uncertainty quantification in seismic inversion by integrating ensemble methods with importance sampling. By leveraging ensemble approach in combination with importance sampling, we enhance the accuracy of uncertainty analysis while maintaining computational efficiency. The method involves initializing each model in the ensemble with different weights, introducing diversity in predictions and thereby improving the robustness and reliability of the inversion outcomes. Additionally, the use of importance sampling weights the contribution of each ensemble sample, allowing us to use a limited number of ensemble samples to obtain more accurate estimates of the posterior distribution. Our approach enables more precise quantification of uncertainty in velocity models derived from seismic data. By utilizing a limited number of ensemble samples, this method achieves an accurate and reliable assessment of uncertainty, ultimately providing greater confidence in seismic inversion results.
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction
Authors: Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia
Date: 2024-09-11 00:00:00
Tasks: All, Link Prediction, Prediction

Item recommendation (the task of predicting if a user may interact with new items from the catalogue in a recommendation system) and link prediction (the task of identifying missing links in a knowledge graph) have long been regarded as distinct problems. In this work, we show that the item recommendation problem can be seen as an instance of the link prediction problem, where entities in the graph represent users and items, and the task consists of predicting missing instances of the relation type <<interactsWith>>. In a preliminary attempt to demonstrate the assumption, we decide to test three popular factorisation-based link prediction models on the item recommendation task, showing that their predictive accuracy is competitive with ten state-of-the-art recommendation models. The purpose is to show how the former may be seamlessly and effectively applied to the recommendation task without any specific modification to their architectures. Finally, while beginning to unveil the key reasons behind the recommendation performance of the selected link prediction models, we explore different settings for their hyper-parameter values, paving the way for future directions.
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Authors: Pavan Seshadri, Shahrzad Shashaani, Peter Knees
Date: 2024-09-11 00:00:00
Tasks: Contrastive Learning, Music Recommendation, Sequential Recommendation, Session-Based Recommendations

Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent manner -- has been an emerging topic in current literature. User feedback -- a positive or negative response to the item presented -- is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function. We propose a sequence-aware contrastive sub-task to structure item embeddings in session-based music recommendation, such that true next-positive items (ignoring skipped items) are structured closer in the session embedding space, while skipped tracks are structured farther away from all items in the session. This directly affects item rankings using a K-nearest-neighbors search for next-item recommendations, while also promoting the rank of the true next item. Experiments incorporating this task into SoTA methods for sequential item recommendation show consistent performance gains in terms of next-item hit rate, item ranking, and skip down-ranking on three music recommendation datasets, strongly benefiting from the increasing presence of user feedback.
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM
Authors: Qijiong Liu, Jieming Zhu, Lu Fan, Zhou Zhao, Xiao-Ming Wu
Date: 2024-09-11 00:00:00
Tasks: Language Modelling, Large Language Model, Quantization

Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content information and generalize to long-tail or cold-start items. Recently, semantic tokenization has been proposed as a promising solution that aims to tokenize each item's semantic representation into a sequence of discrete tokens. In this way, it preserves the item's semantics within these tokens and ensures that semantically similar items are represented by similar tokens. These semantic tokens have become fundamental in training generative recommendation models. However, existing generative recommendation methods typically involve multiple sub-models for embedding, quantization, and recommendation, leading to an overly complex system. In this paper, we propose to streamline the semantic tokenization and generative recommendation process with a unified framework, dubbed STORE, which leverages a single large language model (LLM) for both tasks. Specifically, we formulate semantic tokenization as a text-to-token task and generative recommendation as a token-to-token task, supplemented by a token-to-text reconstruction task and a text-to-token auxiliary task. All these tasks are framed in a generative manner and trained using a single LLM backbone. Extensive experiments have been conducted to validate the effectiveness of our STORE framework across various recommendation tasks and datasets. We will release the source code and configurations for reproducible research.
Negative Sampling in Recommendation: A Survey and Future Directions
Authors: Haokai Ma, Ruobing Xie, Lei Meng, Fuli Feng, Xiaoyu Du, Xingwu Sun, Zhanhui Kang, Xiangxu Meng
Date: 2024-09-11 00:00:00
Tasks: Recommendation Systems, Survey

Recommender systems aim to capture users' personalized preferences from the cast amount of user behaviors, making them pivotal in the era of information explosion. However, the presence of the dynamic preference, the "information cocoons", and the inherent feedback loops in recommendation make users interact with a limited number of items. Conventional recommendation algorithms typically focus on the positive historical behaviors, while neglecting the essential role of negative feedback in user interest understanding. As a promising but easy-to-ignored area, negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors, emerging as an inescapable procedure in recommendation. In this survey, we first discuss the role of negative sampling in recommendation and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in recommendation and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse recommendation scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.
Adversarial Attacks to Multi-Modal Models
Authors: Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, Minghong Fang
Date: 2024-09-10 00:00:00

Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superior performance in downstream tasks compared to their unimodal counterparts. Recent study showed that the attacker can manipulate an image or audio file by altering it in such a way that its embedding matches that of an attacker-chosen targeted input, thereby deceiving downstream models. However, this method often underperforms due to inherent disparities in data from different modalities. In this paper, we introduce CrossFire, an innovative approach to attack multi-modal models. CrossFire begins by transforming the targeted input chosen by the attacker into a format that matches the modality of the original image or audio file. We then formulate our attack as an optimization problem, aiming to minimize the angular deviation between the embeddings of the transformed input and the modified image or audio file. Solving this problem determines the perturbations to be added to the original media. Our extensive experiments on six real-world benchmark datasets reveal that CrossFire can significantly manipulate downstream tasks, surpassing existing attacks. Additionally, we evaluate six defensive strategies against CrossFire, finding that current defenses are insufficient to counteract our CrossFire.
Dual Adversarial Perturbators Generate rich Views for Recommendation
Authors: Lijun Zhang, Yuan YAO, Haibo Ye
Date: 2024-08-26 00:00:00
Tasks: Contrastive Learning, Graph Learning, Recommendation Systems

Graph contrastive learning (GCL) has been extensively studied and leveraged as a potent tool in recommender systems. Most existing GCL-based recommenders generate contrastive views by altering the graph structure or introducing perturbations to embedding. While these methods effectively enhance learning from sparse data, they risk performance degradation or even training collapse when the differences between contrastive views become too pronounced. To mitigate this issue, we employ curriculum learning to incrementally increase the disparity between contrastive views, enabling the model to gain from more challenging scenarios. In this paper, we propose a dual-adversarial graph learning approach, AvoGCL, which emulates curriculum learning by progressively applying adversarial training to graph structures and embedding perturbations. Specifically, AvoGCL construct contrastive views by reducing graph redundancy and generating adversarial perturbations in the embedding space, and achieve better results by gradually increasing the difficulty of contrastive views. Extensive experiments on three real-world datasets demonstrate that AvoGCL significantly outperforms the state-of-the-art competitors.
Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis
Authors: Mohammad Belal, James She, Simon Wong
Date: 2023-06-18 00:00:00
Tasks: Sentiment Analysis, text annotation

Sentiment analysis is a well-known natural language processing task that involves identifying the emotional tone or polarity of a given piece of text. With the growth of social media and other online platforms, sentiment analysis has become increasingly crucial for businesses and organizations seeking to monitor and comprehend customer feedback as well as opinions. Supervised learning algorithms have been popularly employed for this task, but they require human-annotated text to create the classifier. To overcome this challenge, lexicon-based tools have been used. A drawback of lexicon-based algorithms is their reliance on pre-defined sentiment lexicons, which may not capture the full range of sentiments in natural language. ChatGPT is a new product of OpenAI and has emerged as the most popular AI product. It can answer questions on various topics and tasks. This study explores the use of ChatGPT as a tool for data labeling for different sentiment analysis tasks. It is evaluated on two distinct sentiment analysis datasets with varying purposes. The results demonstrate that ChatGPT outperforms other lexicon-based unsupervised methods with significant improvements in overall accuracy. Specifically, compared to the best-performing lexical-based algorithms, ChatGPT achieves a remarkable increase in accuracy of 20% for the tweets dataset and approximately 25% for the Amazon reviews dataset. These findings highlight the exceptional performance of ChatGPT in sentiment analysis tasks, surpassing existing lexicon-based approaches by a significant margin. The evidence suggests it can be used for annotation on different sentiment analysis events and taskss.
An adaptative differential evolution with enhanced diversity and restart mechanism
Authors: Xin Lin
Date: 2024-09-01 00:00:00
Proceeding: journal 2024 9
Tasks: Diversity

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.
Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimization
Authors: Xin Lin
Date: 2024-06-28 00:00:00
Proceeding: journal 2024 6
Tasks: Diversity, Evolutionary Algorithms

Surrogate-Assisted Evolutionary Algorithms (SAEAs) integrate Evolutionary Algorithms (EAs) with surrogate models to reduce the actual number of expensive function evaluations and have been widely used in solving Expensive Optimization Problems (EOPs). However, because of the insufficient diversity of new solutions, SAEAs often fall into local optima and result in evolutionary stagnation when dealing with complex problems. To improve the diversity of new solutions, this paper introduces a Surrogate-Assisted evolutionary Framework with an ensemble of Teachinglearning and Differential evolution (SAF-TD), which successfully integrates the strengths of both EAs to produce more diverse and effective solutions. The main contributions are summarized as follows: First, a novel framework that effectively integrates two distinct EAs using a radial basis function surrogate model and strategic sampling is introduced. Second, a mutation strategy with a supervisory mechanism is proposed to enhance mutation effectiveness and avoid stagnation. Third, a volatility index derived from dimensional improvements is incorporated into parameter control to address fitness-dependent weaknesses. Experiments on five expensive optimization functions and the CEC2017 test suite across multiple dimensions were conducted to evaluate the performance of SAF-TD. The results demonstrate that SAF-TD is highly competitive compared with state-of-the-art SAEAs and exhibits excellent performance in solving EOPs.
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiency
Authors: Xin Lin
Date: 2024-06-26 00:00:00
Proceeding: journal 2024 6
Tasks: Diversity

Differential Evolution (DE) is a simple but powerful population based evolutionary algorithm, which was widely used to solve various complex optimization problems. However, even recently proposed state-of-the-art DE variants tend to get trapped in local minima due to insufficient population diversity in the later stages of evolution. In this paper, an Adaptive Cluster Division Differential Evolution (ACD-DE) algorithm was proposed to mitigate population diversity deficiency. The main highlights are summarized as follows: Firstly, a novel cluster division based mutation strategy was proposed to enhance the diversity of difference vectors during the mutation operation. Secondly, a population diversity detection indicator was used to assess the stagnation level of individuals, enabling stagnation management to be launched based on different stagnation levels. Thirdly, an effective evolution guidance mechanism was proposed by adjusting certain parameters in the 𝐷-dimensional vectors of outlier individuals that significantly deviate from the current population. Fourthly, novel parameter adaptations were employed to dynamically adjust the control parameters, including the scale factor 𝐹 , crossover rate 𝐶𝑅, and population size 𝑃𝑆, during evolution. To validate the ACD-DE algorithm, extensive experiments were conducted on 88 benchmark functions from the CEC2013, CEC2014, and CEC2017 test suites, focusing on the optimization accuracy, convergence speed, time complexity, and component effectiveness. The results demonstrate the superiority of our ACD-DE algorithm compared to state-of-the-art DE variants.
Market information of the fractional stochastic regularity model
Authors: Daniele Angelini, Matthieu Garcin
Date: 2024-09-11 00:00:00
Tasks: model

The Fractional Stochastic Regularity Model (FSRM) is an extension of Black-Scholes model describing the multifractal nature of prices. It is based on a multifractional process with a random Hurst exponent $H_t$, driven by a fractional Ornstein-Uhlenbeck (fOU) process. When the regularity parameter $H_t$ is equal to $1/2$, the efficient market hypothesis holds, but when $H_t\neq 1/2$ past price returns contain some information on a future trend or mean-reversion of the log-price process. In this paper, we investigate some properties of the fOU process and, thanks to information theory and Shannon's entropy, we determine theoretically the serial information of the regularity process $H_t$ of the FSRM, giving some insight into one's ability to forecast future price increments and to build statistical arbitrages with this model.
Identifying key locations for shallow geothermal use in Vienna
Authors: Carolin Tissen a, *, Kathrin Menberg a, Susanne A. Benz b, Peter Bayer c, Cornelia Steiner d, Gregor Gotzl € d, Philipp Blum
Date: 2020-10-23 00:00:00
Proceeding: Renewable Energy 2020 10
Tasks: energy management

Decarbonising the heating sector is crucial for reducing CO2 emissions. This is in particular true for
Central European cities such as Vienna, where 28% of the total CO2 emissions are caused by the energy
supply for buildings. One promising option for environmental friendly heat supply is the use of shallow
geothermal energy. To determine whether shallow geothermal systems are a feasible option to meet the
urban heating demand, the Python tool GeoEnPy is developed and applied to a case study in Vienna. It
allows the evaluation of the anthropogenic heat input into the subsurface, the theoretical sustainable
potential, the technical geothermal potential, and the heat supply rate. The overall heat flow in Vienna is
17.6 PJ/a, which represents 38% of the current heating demand or indeed 99% once all buildings are
thermally refurbished. The technical geothermal potential can satisfy the current heating demand for
63% (BHE system) or rather 8% (GWHP system) of the city area. GeoEnPy reveals that BHE systems are
most feasible in the eastern and southern districts of Vienna. Our findings can guide integration of
shallow geothermal use in spatial energy management focused on key locations to supply buildings with
decentralised and sustainable heat from the subsurface.
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
Authors: Yangfan Hu, Qian Zheng, Guoqi Li, Huajin Tang, Gang Pan
Date: 2024-08-19 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress in fields such as computer vision, speech recognition, and natural language processing. Moreover, the recent success of large language models (LLMs) has fueled a surge in research on large-scale neural networks. However, the escalating demand for computing resources and energy consumption has prompted the search for energy-efficient alternatives. Inspired by the human brain, spiking neural networks (SNNs) promise energy-efficient computation with event-driven spikes. To provide future directions toward building energy-efficient large SNN models, we present a survey of existing methods for developing deep spiking neural networks, with a focus on emerging Spiking Transformers. Our main contributions are as follows: (1) an overview of learning methods for deep spiking neural networks, categorized by ANN-to-SNN conversion and direct training with surrogate gradients; (2) an overview of network architectures for deep spiking neural networks, categorized by deep convolutional neural networks (DCNNs) and Transformer architecture; and (3) a comprehensive comparison of state-of-the-art deep SNNs with a focus on emerging Spiking Transformers. We then further discuss and outline future directions toward large-scale SNNs.
LSegDiff: A Latent Diffusion Model for Medical Image Segmentation
Authors: Vu Quoc Hung, Tran Le Phuong Thao, Trinh Xuan Minh, Dinh Viet Sang
Date: 2023-12-07 00:00:00
Proceeding: SOICT 2023: The 12th International Symposium on Information and Communication Technology 2023 12
Tasks: Decoder, Image Generation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Initially designed for image generation, diffusion models can also be effectively applied to various tasks, including semantic segmentation. However, most existing diffusion-based approaches for semantic segmentation operate in high-dimensional pixel space, demanding a lot of computing and memory resources during training and inference. This paper makes the first attempt to utilize latent diffusion models for semantic segmentation. Specifically, we propose a fast yet effective latent diffusion model and valuate it on medical image segmentation tasks. Firstly, we train a Variational Autoencoder (VAE) network to convert binary image masks into compact latent vectors. The diffusion process can then be executed in this low-dimensional latent space and thus drastically accelerated. Subsequently, we employ the VAE decoder to reconstruct a precise prediction map from the latent output vector produced by the diffusion process. Eventually, we refine the final segmentation results through a straightforward post-processing step using morphological operations. We report our results on two public datasets, including colon polyp images and skin cancer images. Experiments show that our approach achieves competitive accuracy compared to traditional diffusion models while having much better training and inference speed, as well as much more efficient memory consumption.